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More Than Data Stories: Broadening the Role of
Visualization in Contemporary Journalism
Yu Fu and John Stasko
Abstract—Data visualization and journalism are deeply connected. From early infographics to recent data-driven storytelling,
visualization has become an integrated part of contemporary journalism, primarily as a communication artifact to inform the general
public. Data journalism, harnessing the power of data visualization, has emerged as a bridge between the growing volume of data and
our society. Visualization research that centers around data storytelling has sought to understand and facilitate such journalistic
endeavors. However, a recent metamorphosis in journalism has brought broader challenges and opportunities that extend beyond
mere communication of data. We present this article to enhance our understanding of such transformations and thus broaden
visualization research’s scope and practical contribution to this evolving field. We first survey recent significant shifts, emerging
challenges, and computational practices in journalism. We then summarize six roles of computing in journalism and their implications.
Based on these implications, we provide propositions for visualization research concerning each role. Ultimately, by mapping the roles
and propositions onto a proposed ecological model and contextualizing existing visualization research, we surface seven general topics
and a series of research agendas that can guide future visualization research at this intersection.
Index Terms—journalism, data visualization, computational journalism, data-driven storytelling
1 INTRODUCTION
D
ATA interacts with journalism in many ways. One of
the most important ways is the communication of data,
frequently performed through charts and visualizations.
Newspapers were among the first media to bring infograph-
ics to the public’s attention, with well-known examples like
USA Today’s Snapshots, which often employed embellished
and straightforward graphics [1]. More recently, enhanced
web-based interactive visualization technologies, notably
D3.js [2], have given rise to new forms to communicate
data stories. Mainstream news platforms like The Guardian,
The New York Times, and The Washington Post were early
adopters and trailblazers of such Web-based interactive and
dynamic visual communication. Following them, a new
generation of websites, such as FiveThirtyEight and the
Pudding, has emerged in the midst of this wave, progres-
sively gaining attention from broader audiences.
While data journalism exhibits promise, conventional
journalism has been facing increasing challenges and turbu-
lence [3], [4]. The story depicted in HBO’s series The News-
room about how a cable news network fights new business
models, arising citizen journalism, and online journalism to
preserve their editorial autonomy and journalistic integrity
is fictitious and some see it as an elitist presentation of
journalism but the diverse threats faced by journalism
and its sweeping transformation are reflective of reality.
Over the past decades, journalism has undergone various
changes driven by multiple social and technological forces,
particularly the digitalization of the media environment.
Journalism has long been our primary source of infor-
mation, a channel that connects us to the world outside
our tangible surroundings, a social glue that binds people
together in our “imagined community” [5], [6]. In many
countries, it is closely tied to democracy and a “watchdog”
role to scrutinize powerful institutions, be they govern-
ments or companies [7]. Journalism’s historical importance
is undeniable, as illustrated by Pulitzer’s famous remark
“Our Republic and its press will rise or fall together and
Burke’s reference to it as “Fourth Estate”.
However, contemporary journalism has experienced a
crisis in perceived importance and credibility, as manifested
by the decline in its overall financial health and audience
trust [8], [9], [10], [11]. Online journalism and citizen jour-
nalism, both spawned by digital technologies, have been
assigned blame due to emphasizing speed and undermining
journalism’s professional boundaries and its conventional
pursuit of objectivity. These are not the lone challenges
our increasingly complicated world and the explosive
growth of information transform journalists’ role into a
more challenging one a filter, a transmitter, an organizer,
an interpreter, and a fact-deliverer [12]. Technology has also
brought opportunities hypermedia/multimedia content
that provides better context, interactivity that allows more
agency of audiences and potentially sparks engagement,
and platforms that enable ordinary people to participate
in civic dialogues. Journalists have turned to computa-
tional technologies to counter their role transformation. The
early adoption of computer-assisted reporting leveraging
social science statistics won investigative journalists mul-
tiple Pulitzer prizes. Data journalism featuring data-driven
storytelling and interactive visualization dashboards has re-
cently garnered mainstream attention and played a vital role
in combating the pandemic and informing the public [13].
Using data to tell stories has drawn significant atten-
tion from the visualization research community[14], [15],
[16], with influential researchers advocating for its impor-
tance [17], [18]. Data storytelling has likely become the
most prominent topic at the Journalism-Visualization inter-
section, and rightfully so. However, existing visualization
research centering around data storytelling (e.g., [16]) tends
to focus on data journalists/platforms and their audiences,
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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Fig. 1: An overview of the paper structure and contributions
i.e., those who are already comfortable with data communi-
cation. We consequently lack a comprehensive understand-
ing of other journalists and audiences with limited data
infrastructure, skills, experience or literacy. Furthermore,
data-driven storytelling emphasizes the communicative role
of visualization, whereas journalists’ tasks extend beyond
mere communication. In addition, the traditional perspec-
tive regarding news organizations as fixed institutions does
not adequately consider today’s increasingly dynamic and
participatory information environment.
We believe it is an appropriate time to review the state of
contemporary journalism and explore how data visualiza-
tion research can contribute to the discipline’s growth and
evolution. In particular, we ask:
In light of the recent shifts in the information land-
scape and journalistic metamorphosis, what new vi-
sualization research opportunities could emerge at
this intersection?
How could visualization research contextualize its
work to address broader challenges in journalism?
To answer these questions, we conducted an exploratory
scoping review [19], [20] of literature from both journal-
ism study and visualization research. In doing so, we
aim to shed light upon contemporary journalistic contexts
and challenges faced by broader journalists (e.g., pub-
lic/independent journalists) and their audiences, the roles
played by computational technologies in addressing such
challenges, and the unique value that data visualization can
contribute to these roles. Our goal is to provide an updated
and holistic perspective on how visualization research could
aid journalism. This review consists of three major parts,
with each denoting a facet of our contribution, as shown
in Figure 1:
C1. In section 3, we distill a set of important changes
in journalism driven by digital technologies, along with
journalism’s computational turn in response to emerging
challenges. We summarize six roles of computational tech-
nology in contemporary journalism and explain the ratio-
nale behind and implications for computing intervention.
C2. In section 4, we review discourse on the value of
visualization and contextualize these values in journalism.
Building off these discussions on visualization values, we
offer propositions (Figure 2) concerning each of the six roles
and map them onto an ecological model (Figure 3) to further
contextualize them.
C3. In section 5, based on our propositions, we surface
seven general topics and multiple subtopics to encompass
future Journalism-Visualization interdisciplinary research.
For each subtopic, we synthesize existing visualization re-
search, contextualize the work in journalism, and identify
future research agendas.
2 METHODOLOGY
2.1 Review Methodology
Due to the complex and heterogeneous nature of the two
fields and the variety of topics to be covered, we did not
choose a systematic review or a traditional survey. Instead,
our method resembles a scoping review, a relatively new,
but increasingly popular approach for synthesizing research
evidence [19]. HCI researchers have recently adopted scop-
ing reviews on different occasions [21], [22]. Such a review
aims to “map rapidly the key concepts underpinning a research
area and the main sources and types of evidence available” and
is commonly used to understand the range and nature of
research activity, determine the value of a full systematic
review study, summarize and disseminate research findings,
and identify research gaps in the existing literature and
aid formulating future research agendas [23]. Compared to
a traditional systematic review, a scoping review tends to
address broader topics instead of seeking answers to specific
research questions. According to CIHR’s Guide to Knowledge
Synthesis, scoping reviews “often do not undertake a detailed
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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appraisal of identified evidence sources and detailed synthesis of
the results of the studies” [20].
We used Google Scholar as our primary search plat-
form since the literature we need to review spreads across
two fields and their sub-domains. Our review was an it-
erative process, and we progressively expanded and re-
vised our search term pool during the process. We began
by searching for literature that discusses journalism more
comprehensively, focusing on the changes and challenges
brought about by technological developments. Therefore,
we started with broader terms such as “journalism studies”,
“journalism + new media”. Since these terms often led
to vast and homogeneous literature, we tended to follow
highly cited work. In journalism, these are often books that
cover various topics. We skimmed through the books and
reviewed the chapters and content that we deemed relevant
for example, we included Journalism, Trust, and Credibility
and excluded Journalism Education from The Handbook of
Journalism Studies [24]. We distilled themes (e.g., participa-
tory journalism, interactive journalism, news personaliza-
tion, journalistic routine, journalism and trust, etc.) from
our initial review phase, and used these themes for our
second-round search. Our third round search focused on the
computational exploration in journalism; our search terms
include area-based ones like “computer-assisted reporting”,
“precision journalism”, “data (-driven) journalism”, “com-
putational journalism”, and topic-based ones, like “auto-
mated journalism”, “algorithmic accountability + journal-
ism”, “misinformation”, “fact-checking in journalism”, and
so on. We also expanded our pool by following leading
researchers in the respective areas (e.g., Meyer, Diakopoulos,
Lewis) and using snowballing procedures [25]. This liter-
ature is mostly peer-reviewed articles. As for the InfoVis
literature, we expanded our literature pool by searching
matching visualization keywords (e.g., “augmenting video
+ visualization”) and from our personal collections. We
also searched the ACM digital library and the IEEE Xplore
library. The literature we reviewed across these two fields
and multiple topics included 187 documents. Table 1 shows
the distributions of publication venues of literature from
both fields. We submit the list as supplementary materials.
TABLE 1: Literature Venues
Publication Venue Count
IEEE TVCG 37
ACM CHI 16
ACM UIST 4
IEEE CGA 4
ACM BELIV 4
Others 28
Total 93
(a) Visualization Literature
Publication Venue Count
Digital Journalism 11
Journalism Practice 4
New Media & Society 3
Journalism 2
Journalism Studies 2
Others 72
Total 94
(b) Journalism Literature
2.2 Reflexivity
This study is based on existing work that spreads across
different topics in these two large fields. While we strove to
be unbiased during the process, our experiences unavoid-
ably affected how we screened, organized, and presented
the work we reviewed. The first author has multiple years
of experience as a professional/independent journalist and
is now a visualization researcher. The second author is a
seasoned visualization researcher. Our experiences in these
two fields lend us a unique yet balanced perspective on the
challenges and opportunities faced by broader journalists
and audiences, as well as the value visualization can pro-
vide. While we present a series of potential technological
solutions, we do not endorse a technological determin-
ism/solutionism perspective. Rather, we believe visualiza-
tion technology simply offers another option that has its
unique merits and shortcomings their impact on pub-
lic communication awaits further investigation. We would
also point out a limitation of our study the journalism
literature we reviewed is primarily about journalism in the
Western world, but journalism in other countries may have
distinctly different roles and characteristics.
3 JOURNALISMS DIGITAL METAMORPHOSIS AND
COMPUTATIONAL TURN
Since entering the digital age, journalism has undergone
a continuous metamorphosis. A new form of journalism
featuring ubiquitous news, global information access, in-
stantaneous reporting, interactivity, multimedia content,
and customized content has arisen [9], [26], followed by
social media, offering the promise of the “biggest audience
reach” and a “perfect public sphere where everyone can have
a voice” [27]. Such a metamorphosis pervades and affects
many aspects associated with journalism.
In this section, we first highlight four salient transfor-
mations driven by more general developments in technol-
ogy and discuss the challenges accompanying them. Next,
drawing from digital journalism and computational journal-
ism literature, we introduce how journalism practitioners
and scholars have proactively pursued computing tech-
nologies to aid journalism and their different perspectives.
Ultimately, by synthesizing such shifts, we summarize six
roles that computational technologies play in contemporary
journalism, along with the rationales and implications.
3.1 Salient Transformations in Digital Era
Recent shifts in journalism span multiple dimensions, rang-
ing from the nature of news content to news dissemination,
from internal organizational structures to external infor-
mation environments. Specifically, we highlight these four
salient transformations:
Enriched Media Content: The transformation in news
content should not be alien to most audiences. New capa-
bilities such as interactivity, on-demand access, user con-
trol, and personalization coupled with enriched and hyper-
linked multimedia have driven digital journalistic content
to be more contextualized, navigatable, and interactive [9],
[28], [29]. Amidst such changes, one relevant instance we
highlight is interactive journalism multilayered visual
storytelling that engages users in interaction to unfold the
narratives [30]. Such interactive features not only have the
potential to help journalists establish authority through
enhanced visuals and sophisticated data [30], but also pro-
vide enjoyment that can re-engage audiences [29], [30] and
expand ways of thinking about journalism [30].
Participatory Environment: The Internet, digital media
technologies, and social media create the conditions for
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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Transformations Challenges
(T1) Interactive Journalism
C1.1: creating interactive content requires different skills than those typically acquired by
traditional journalists, notably programming skills [30]
C1.2: audiences may be unaware of or less motivated to exploit rich interactivity [29]
(T2) Participatory Journal-
ism and Communication
Environment
C2.1: it weakens journalism’s occupation boundary and roles [31], upsets its previous institu-
tional power on gatekeeping [4], [32], [33], and diminishes its traditional authoritative voice [30]
C2.2: it lowers the threshold for producing and distributing news and potentially enables an
influx of misinformation or even hate speech[34]
C2.3: it is challenging to enable more coherent, elevated online discussion [35]
(T3) A Loaded and Polluted
Infosphere
C3.1: it creates a polluted public sphere where information is often met with skepticism [10]
C3.2: it places a “heavy mental burden” on the audience to distinguish trustworthy information
from a sea of misinformation [10]
(T4) News Personalization
C4: inadequate awareness of such filtering and prioritization could exacerbate the “filter
bubble” phenomenon or algorithmic biases [36], [37], [38]
TABLE 2: Relevant Journalistic Transformations and The Emerging Challenges
people formerly known as “the audience” to enter jour-
nalism by enabling them to gather, create, and distribute
information in a much easier and more effective manner [6],
[27], giving birth to participatory journalism, or citizen
journalism. While participatory journalism promises to en-
gage and connect people [39] and provides a more inclusive
public communication environment that encourages citizens
to play an active role [40], [41], it nevertheless weakens the
occupation boundary and roles [31], upsets journalism’s pre-
vious institutional power on gatekeeping [4], [32], [33], and
diminishes traditional journalism’s authoritative voice [30].
The public’s competence to participate in journalistic pro-
duction and civic affairs, however, varies widely [42], as do
their interests in doing so. Technological advances have also
facilitated other participatory activities, such as quizzing
(e.g., polls, questionnaires), voicing one’s own opinion, and
commenting [42]. News consumers have welcomed and
grown accustomed to a more symmetrical communication
mode [42], [43], changing the traditional journalist-audience
relationship from a one-way, asymmetrical communication
model to a two-way dialogue [42], [44].
A Loaded and Polluted Infosphere: A more complex
world and an explosive growth of data have significantly
complicated journalists’ roles [45]. Further, the lowered
threshold has let in a flood of information, laced with a
plethora of misinformation, conspiracy theories, and fake
news people constantly need to distinguish trustworthy
information from them [45]. In addition, a more sensation-
alistic and politically biased coverage led by competitive
business and a polarized political environment has further
eroded audiences’ trust in media [9], [10]. Collectively, these
factors have molded a loaded and polluted infosphere
where information is often met with skepticism [10], making
journalism’s role as information gatekeeper more important,
yet increasingly challenging.
News Personalization: To counter the enormous vol-
ume and multiplicity of information that overwhelms audi-
ences, tech companies like Google and Meta (Facebook) are
spearheading efforts to deliver tailored news content using
personalization algorithms fed by user data [37], [46], [47].
Personalization has grown popular among news publishers
as well [48], worrying scholars as it could largely reduce
serendipitous news discovery and exposure to alternative
viewpoints [37], thus exacerbating the “echo chamber” phe-
nomenon [36], [37], [38]. While other researchers have pre-
sented counter-evidence [49], [50] or advocated the positive
impact of news personalization [48], its potential to regulate
people’s belief systems and social influence has attracted
wide attention from researchers in different fields.
Apart from the aforementioned changes, generative AI
systems (e.g., large language models) and their applications,
with their rapidly evolving ability to perform tasks once
within the purview of journalists, have started to exert a per-
vasive influence on journalism and our society as a whole.
The ensuing transformation is unfolding before our eyes
and carries great potential to revolutionize journalism anew.
On the one hand, it is foreseeable that such AI applications
(e.g., ChatGPT) will be capable of assisting journalists with
many tasks, particularly text content generation. On the
other hand, this generative AI could exacerbate information
pollution by reducing the cost and time to produce misin-
formation/disinformation.
Regardless, further systematic investigation is needed to
identify the emerging challenges and opportunities as well
as the long-term effect generative AI technologies pose to
journalism and the journalistic environment.
3.2 Understanding Journalism’s Computational Turn
Like many other fields, journalism has embarked on its own
journey of computing innovations to accommodate its mul-
tifaceted shifts. According to Coddington’s typology [51],
its computational practices have occurred in different forms:
computer-assisted reporting, data journalism, and computa-
tional journalism.
Computer-assisted Reporting (CAR): With pioneers like
Philip Meyer [12], [54], CAR marks the entry of computing
into journalism. CAR journalists transitioned from main-
frame machines to personal computers, from simply sorting
numbers or searching for information to more sophisticated
database management and statistical analysis. Particularly,
CAR enriched the content and boosted the credibility of
investigative journalism [51]. Combined with the emergence
of the Web, CAR evolved into its second generation. On
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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Forms Perspectives
Computer-assisted
Reporting (CAR)
- associated closely with professional investigative reporting [51], [52], [53]
- leverages scientific methods and statistics to enhance journalism’s credibility [12], [51], [54]
- harnesses the computer’s power to manage data, conduct statistical analysis [12], [51]
Data Journalism
- places data as an information source [55] and a core in the journalistic workflow (curation, analysis,
communication, etc.) [51], [52]
- develops news applications that allow readers to access and explore data [55], [56], [57]
- democratizes journalistic resources, tools, and methodologies [58] and invites non-journalists to
participate in public data-driven analysis and communication [51], [58]
Computational
Journalism
- leverages computer’s processing capabilities to aggregate, automate, and abstract information [59],
make sense of information, and free journalists from low-level work [60]
- critically examines computation’s influence on journalism practice, content, and reception [53], [61],
with a focus on transparency and accountability of journalism’s algorithms [62], [63]
TABLE 3: Journalism’s Computational Practices and Their Perspectives
the one hand, more general journalists began to acquire ba-
sic computer-based skills and technologies for information
gathering; on the other hand, those CAR pioneers embarked
on more sophisticated tools for data gathering and statistical
analysis. The latter form arguably evolves into the con-
temporary approaches: data journalism and computational
journalism [51], [52].
Data Journalism: The relationship between data jour-
nalism and CAR also remains debated [64]. Bounegru [58]
suggests two main differences between them. One lies in the
role of data in news production CAR stresses using data
as a means to enhance reporting with a focus on journalistic
tasks, while data journalism emphasizes data itself and its
function within the whole journalistic workflow. Another
difference lies more in the overall data environment — CAR
took place when digital data was scarce, and journalists
needed to actively seek information to answer their ques-
tions, whereas now, a vast amount of data has overwhelmed
us, and making sense of it comes as a priority [58]. Data
journalism has also delved into broader journalistic prac-
tices, while CAR is more rooted in investigative journal-
ism [51]. Parasie and Dagiral suggest that data journalism
challenges traditional CAR’s epistemological model as it
attempts to decentralize the construction of moral claims
using data analysis and consider readers as “legitimate and
active contributors” [65]. It echoes Holovaty’s early calling
for journalism to abandon the story-centric worldview and
accept computer programming as a form of journalism [57].
Despite that scholars and practitioners championing its
importance in today’s society due to an array of benefits,
including its capability to shift journalistic context, foster
the development and application of news apps, and fa-
cilitate trustworthy journalism [55], [56], data journalism
encountered its obstacles, evidenced by the shutdown of the
EveryBlock due to its inability to engage audiences “pages
of data weren’t enough to engage the public on their own” [55].
As Kovach and Rosenstiel [38] remark, “Journalism must
make the significant interesting and relevant” and “Engagement
should be seen as being part of journalism’s commitment to the
citizenry”, while traditional journalists rely on instinctive
judgments and superior writing, data journalism starts to
seek for solutions from information and HCI studies [66].
Computational Journalism: Computational journalism
has emerged as an academic field to investigate how to
advance news gathering and improve journalism workflow
via computational technologies [67]. Hamilton and Turner
define computational journalism as “the combination of algo-
rithms, data, and knowledge from the social sciences to supplement
the accountability function of journalism” [68]. Cohen et al.[69]
prioritize computational tools that can boost journalistic
accountability and bring positive social impact. Researchers
in this field have proposed refined conceptions and focus:
Diakopoulos suggests that computational journalism focus
on the processing capabilities of computing, notably, the
capabilities to aggregate, automate, and abstract informa-
tion [59]; Flew et al. [60] underscore computational jour-
nalism’s aim to facilitate sensemaking in journalism and
its capability to free journalists from the low-level work
(i.e., discovering/obtaining facts) as well as enhance user
engagement and enable richer user interaction with news
media. Thurman [53] notes that the scope of computational
journalism has expanded over the years, with an emphasis
on human-centric considerations and examining the use of
technological artifacts and influence in journalism [70].
3.3 Roles for Computing in Journalism
Schudson lists six functions journalism has served, partic-
ularly in a democratic society: informing the public, in-
vestigating to keep power accountable, analyzing to help
citizens comprehend a complicated world, promoting so-
cial empathy to allow other viewpoints to be appreciated,
providing a public forum for dialogue among citizens, and
mobilizing people to support certain programs [71], [72],
[73]. Our previous discussions suggest that there are various
angles for computing to support such functionalities and
respond to the challenges we identified in Table 2: techno-
logical advances to make certain tasks possible, HCI per-
spectives to accommodate the specific needs of journalists,
information and communication perspectives to understand
news/information experience and how information dis-
seminates, and critical lens to examine technology’s social
impacts. Drawing on Abebe et al.’s discourse on roles for
computing in social change [74], we summarize six roles that
computing plays in journalism that go beyond the general
use of technology (e.g., smartphones, search engines) and
explain their respective emphases:
Communicator Computer-enabled artifacts that
communicate (i.e., engage/inform/affect) insights or narra-
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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tives to audiences. Such artifacts underscore computing’s
contribution to enriching news content. They range from
simpler multimedia news [44] to more sophisticated inter-
active content (e.g., data-driven stories) [30]. The latter are
mostly products of interactive journalism or data journalism
and inherit their characteristics and ethos. Computing as
Communicator concerns the design of information products
for the public that can better inform and engage them.
Facilitator Tools that facilitate news production,
including news gathering, filtering, fact-checking, and par-
ticularly, content authoring. In its broader sense, Facilitator
could encompass computational applications that facilitate
various responsibilities throughout the news production
pipeline, from handwriting transcription [9], [68] to data
wrangling (e.g., Google Refine) [69]. The rise of interactive
content and enriched media types is challenging the long-
standing supremacy of written text, compelling journalists
to rely more heavily on technology to create computer-
enabled content. Hence, we focus on a more specific in-
terpretation of Computing as Facilitator, which attends to
journalists’ growing demands to create enriched journalistic
products (e.g., narrative visualization) by facilitating the
authoring process. Moreover, it is important not to disre-
gard independent/citizen journalists, who are increasingly
assuming an important authoring role.
Analyzer Tools that assist in exploring and making
sense of information, such as extracting salient information,
deriving statistical characteristics, inferring trends, and
constructing insights/narratives. While Analyzer can be
considered a specialized type of Facilitator that assists in
data analysis, we classify it as a separate category. Such tools
have existed since the early days of CAR and have been
adopted by data journalism and computational journalism
in their respective ways. Computational journalism’s per-
spective focuses on aiding journalists with analytical activi-
ties, such as combining information from various sources,
extracting information, and exploring and mining docu-
ments [68], [69]. One specific example that Cohen et al. men-
tioned is to analyze and visualize the relationships among
entities across a document collection [69]. Data journalism’s
perspective seeks to provide the public with analytical tools
to interrogate data and develop their own interpretations.
Well-known examples include data dashboards appearing
on news websites. In this context, computing’s role as an-
alyzer can overlap and be blended with its role as commu-
nicator. Computing as Analyzer cares about helping both
journalists and their audiences better analyze information
and obtain insights and how to make analytical activities
more engaging when facing the public.
Public Forum Digital platforms and mechanisms
for dialogue among the public and serving as a common
vehicle for perspectives of different social groups. Kovach
and Rosenstiel state that journalism has always been a forum
for public discourse since its origin in the Greek marketplace,
but today, this role is largely filled and failed by social
media as they enable bad actors to distort, mislead, and
overwhelm [38]. Allowing audiences to comment has been
the primary practice for news organizations to fulfill this
functionality in the digital era [35], [75]. But the emergence
of participatory journalism and enriched content further
complicates this functionality. For example, how could com-
puting facilitate and elevate public discourse around a
COVID data dashboard and drive data-driven conversa-
tions? Such emerging topics continue to await investigation.
Computing as Public Forum focuses on news platform
designs that adapt well to new media forms and enable
more coherent and elevated public discussions.
Automator Computational technology (e.g., algo-
rithms) that automates journalistic tasks (e.g., news deliv-
ery, content creation). Automator includes personalization
algorithms [37], [62], algorithms that auto-generate con-
tent/insights [76], [77], and algorithms that alert journal-
ists/audiences about emerging events/news, etc. In partic-
ular, automated journalism that transforms structured data
to publishable news stories using programs/algorithms has
gained momentum recently, due to its potential to generate
news content in scale and in a faster, less-biased, less error-
prone manner [77]. News magnates such as the Associated
Press have already been publishing automated news con-
tent [77], [78]. Furthermore, generative AI systems have
certainly fueled the development of Automator applica-
tions. Computing as Automator offers an alternative news
production paradigm by freeing journalists from tedious,
often low-level tasks. Although it simultaneously brings
complicated social and economic considerations.
Auditor Technology that uncovers the effect of
algorithms and audits news credibility and biases. As
algorithms have infiltrated more facets of journalism, re-
searchers in computational journalism started to advo-
cate investigations of algorithmic accountability and trans-
parency [62], [63], [79] and using computing as diagnostic
device [61]. Another approach is to employ computing tech-
nology as a safeguard against misinformation. Lazer et al.’s
Science article identifies two categories of intervention
individual empowerment and platform-based detection and
intervention [80]. Computing as Auditor offers the potential
to detect algorithmic biases and audit single/collective news
content for credibility in a more ubiquitous manner, thus
empowering individual readers in today’s overloaded and
polluted infosphere.
4 CONTEXTUALIZING THE VALUE OF VISUALIZA-
TION IN JOURNALISM
The previous section builds up to computing technol-
ogy’s six roles in journalism and their emphases. In this
section, we explore how data visualization can serve these
roles and provide its unique set of values to journalists and
audiences from the perspectives listed in Table 3. To achieve
that, we first explore the fundamental values ascribed to vi-
sualization by researchers/practitioners as a starting point.
4.1 Discourse on the value of visualization
Data visualization is often touted for its value in amplify-
ing human cognition, such as expanding working memory,
enhancing pattern recognition, and making information ma-
nipulable [81]. Norman suggests that visual representations
can largely impact how efficiently we perform tasks [82].
Fekete et al. focus on knowledge generation and propose an
economic model that defines the total cognitive benefits [83].
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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Fig. 2: Propositions for visualization with respect to computing’s roles in journalism
Stasko remarks that the merit of visualization lies in its
capabilities to minimize the time people need to understand
the overall essence of data or discover insights about the
data, and with greater confidence [84]. He also emphasizes
the importance of interaction to these values, concurring
with Elmqvist et al.’s referring to interaction as “the catalyst
for users dialogue with data” [85].
The visualization community has reflected on its roots
in computer science and its tradition of supporting informa-
tion workers with well-defined analytical tasks. Traditional
visualization research often adopts a utilitarian view that
focuses on efficiency, insights, and comprehension [86]. The
utilitarian view sees the functional side of visualization and
worries about aesthetics undermining this functional value,
while the aesthetic view sees its artistic side and fears that
functional foci could bore or even intimidate an audience.
Over the years, visualization researchers have explored
the values of visualization using alternative lenses. For
example, Pousman et al. proposed a new subdomain termed
Casual InfoVis”, differentiating it from the traditional view
of visualization interfaces in four aspects: user population,
usage pattern, data type, and insights [87]. They expanded
the traditional view of visualization for analytic insight, stat-
ing that casual visualizations add awareness insight, social
insight, and reflective insight. In other words, casual visual-
izations can provide information that is less crystalized but
“subtly useful”, information that improves understanding of
a “social group and one’s place in it”, and information that
support “self-contemplating one’s personal and idiosyncratic
thoughts” [87]. Building off Casual InfoVis, researchers have
sought to understand the broader value of visualization
in a leisure context. Danziger [88] advocated “Information
Visualization for the People”, emphasizing regular people’s
growing needs to make sense of information in everyday
life. Sprague and Tory [89] explored people’s casual encoun-
ters with visualizations and uncovered their motivations
for using visualization beyond the utilitarian view, includ-
ing entertainment, curiosity, and social activities. More re-
cently, researchers started focusing on emotional responses
or affective effects of visualizations, particularly those for
communicative purposes [86], [90], [91], [92]. Such affective
influences are tied to the aesthetic value of visualization.
In journalism, visualization is beautifully referred to as
“the functional art” by Cairo [93] as it often embodies both
functional and artistic values. Though Cairo acknowledges
that journalism has a long history of treating infographics as
“mere ornaments” that attracts audiences and underestimates
their functional value. Cohen concurs by stating that the
core value of journalistic visualization is “deeply rooted in
measurable facts” and provides that it “offers a tantalizing
opportunity for storytelling that is above all driven by facts, not
fanaticism” [94]. Data visualization has evolved well beyond
static infographics its aesthetic values and functional
values do not have to be encapsulated within static views
simultaneously . A comprehensive understanding of how to
leverage its aesthetic value to enhance its functional value
is a key for visualization to better support journalism
the exquisiteness of well-made visualizations can enthrall
audiences, and that is “a valuable social currency for sharing
and attracting readers” [94].
4.2 Implications of the Six Roles for Visualization
One seemingly cogent question is how do these values of
visualization relate to computing’s six roles in journalism?
And how can visualization better support or be supported
by these six roles?
First, it is helpful to distinguish journalistic content cre-
ators from recipients (we choose it over journalist/audience
classification since journalists can be information recipients
and ‘audiences’ are able to create content in today’s par-
ticipatory environment). Journalistic content creators are
information workers and need to accomplish comparably
crisp tasks such as gathering, filtering, and analyzing raw
information and then weaving insights and narratives into
a journalistic product. These tasks align better with the
functional value of visualization. Recipients, contrastingly,
do not necessarily need to engage in these tasks — their en-
counters with visualization are leisure and loosely bounded,
thus fitting better with the Casual visualization notion.
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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Fig. 3: An ecological model for mapping computing in contemporary journalism. This model consists of two primary
processes: the news production process that roughly equates to Lee et al.’s storytelling process [18] and the news
consumption process. During the production process, creators explore and analyze data using Analyzers (e.g., visual
analytic systems), organize story pieces, then produce journalistic products using Facilitators (e.g., visualization authoring
tools). The resulting products are Communicators, including infographics, narrative visualizations, or even data videos.
Automators also have the potential to complete this process by generating journalistic content directly from structured data.
The news consumption process starts when audiences encounter these products and decide to engage. Such engagement
with visualization can provide casual (i.e., awareness, social, reflective) insights, curiosity, or simple entertainment. Recent
data-driven storytelling that employs interactivity enables audiences to dive further and engage in preliminary exploration,
invoking visualizations’ value as Analyzers. In today’s participatory environment, if a Public Forum (participatory
opportunity) is provided, interested audiences can easily transition into the creators’ role, triggering further analytical
engagement and completing the circle.
Second, journalism is a type of public communication
the social influence should not be overlooked, especially
in the increasingly participatory journalism environment.
Therefore, we propose to borrow Domingo et al.’s theoret-
ical model on inclusive public communication and derived
analytical grid (access/observation, selection/filtering, pro-
cessing/editing, distribution, interpretation ) [27] as a lens.
Conventionally, visualizations are often employed in
journalism as Communicator journalists use visualiza-
tions, often static infographics, to aid their traditional sto-
rytelling that “seldom lets audience explore, but explains and
conveys ideas already thought out” [95]. But the emerging
data journalism has shifted such ethos by unleashing a
certain level of exploratory freedom to audiences. Result-
ing visualization products include data dashboards, which
allow the public to explore and analyze data indepen-
dently, and data-driven stories, which integrate visualiza-
tion’s exploratory and explanatory power [96] to lead
readers towards a valid interpretation of underlying data [16].
The former is a public-facing Analyzer, and the latter is
a hybrid of Analyzer and Communicator. Journalist-facing
visualization Analyzer also deals with unstructured and
textual data. For example, scholars and practitioners [53],
[69] have highlighted visual analytic tools like Jigsaw [97]
and Overview [98] that can mine insights from documents.
Facilitator in this context aims to facilitate the creation
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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of visualization artifacts. Mainstream generic tools like
ManyEyes [99], Tableau [100], and Datawrapper have been
underlined by scholars from both fields [68], [69], [101],
[102]. However, both parties acknowledge the demands
for more appropriate tools that support and accommodate
journalism’s workflow [69], [102].
The linkages between the other three roles (i.e., Public
Forum, Automator, Auditor) and visualization have mostly
remained modest in practice and experimental in research.
While these three roles are not traditionally associated with
data visualization, their growing importance drives us to ex-
plore and investigate their potential marriage, benefits, and
consequences. To strengthen the existing roles (i.e., Com-
municator, Analyzer, Facilitator) and explore new roles (i.e.,
Public Forum, Automator, Auditor), we offer our proposi-
tions with respect to each role (Figure 2) and map them onto
an ecological model (Figure 3) that takes into account the
distinction of news creators and recipients, visualization’s
analytical and casual values, and Domingo et al.’s public
communication model [27].
5 RESEARCH TOPICS AND AGENDAS
In this section, we present and discuss seven research
topics and their subtopics at the Journalism-Visualization
intersection. These topics are distilled from the propositions
and the ecological model we proposed. For existing roles,
we contextualize recent visualization research and explain
how it can contribute. For unexplored roles, we point out the
research gaps and speculate on potential research directions.
By doing so, we intend to provide a map for future visual-
ization research to position itself and inspire investigations
beyond current mainstream practices.
5.1 Facilitating Data Communication
Subtopic 1.1: Facilitate Narrative Visualization Author-
ing (Facilitator)
Agenda: Support sophisticated and expressive narrative
visualization creation while lowering the barrier
Literature: [103], [104], [105], [106], [107], [108], [109], [110],
[111], [112]
Supporting more sophisticated and expressive interac-
tive visualization creation while lowering the barrier for
data visualization authoring has always been a central focus
for visualization researchers. Visualization researchers have
explored different forms of tools to author engaging nar-
rative visualizations. For example, SketchStory [104] allows
users to sketch narrative visualization in a freeform way
with pen and touch interactions. Ellipsis[113] decouples nar-
rative structure from visualization creation and allows users
to create visualization scenes, add annotations and organize
storylines. Recent tools like Lyra [105], Data Illustrator [106],
and Charticulator [107], focus on expressive authoring and
distinguish themselves from prior template editors and shelf
constructions (e.g., Tableau). According to Satyanarayan et
al. [114], such authoring tools, including subsequent Lyra
2 [108] and Data Animator [109], often assume authors’
familiarity with computational thinking and are already
acquainted with datasets [114]. Journalists’ interests tend to
align with the general direction of research on visualization
authoring tools to lower the threshold (e.g., reducing
programming, data wrangling) and raise the ceiling (e.g.,
improving expressiveness) [114]. Idyll Studio [110], based
on the Idyll markup language [115], exhibits great potential
by lending authors the freedom to write texts and binding
texts with data and variables. Its WYSIWYG-style interface
resembles journalists’ writing environment. It also supports
Vega-Lite [116] charts and dynamic layouts and reactive
triggers that can support techniques like scrollytelling. Fur-
thermore, CrossData [111] automates the establishment of
connections between textual content and underlying data,
streamlining the process of authoring data articles. Data-
Particles [112] also leverages such latent connections to fa-
cilitate authors to create animated unit visualization, further
extending the expressive capabilities of such visualization
authoring tools.
Subtopic 1.2: Bridge the gap between exploration and
presentation (Facilitator)
Agenda: Allow journalists to visually explore data, obtain
insights, then organize the insights into engaging visual
presentations (e.g., narrative visualization) in a more effi-
cient and seamless way
Literature: [117], [118], [119]
A salient feature of journalists’ information intermediary
role is that they need to perform both analytical and commu-
nicative tasks. Journalists are usually knowledgeable of their
domains but less data-savvy. Therefore, data acquisition and
wrangling can prevent them from adopting information
tools traditional journalists are more likely to interact
with information tools through direct manipulation [120]
to find insights and construct narratives [117]. Narrative
visualization authoring tools like Ellipsis [113] and Idyll
Studio [110] do not take data analysis into consideration.
Recently, visualization researchers have advocated a more
seamless and integrated authoring pipeline [18], [117], [118],
[119] where users can perform exploratory data analysis to
discover insights and narratives, then organize them into
narrative visualizations by manipulating layouts, annotat-
ing, highlighting, or even re-configuring visual elements.
Such integration presents new challenges and opportunities.
Gratzl et al. propose the CLUE model, aiming to integrate
data exploration and presentation by capturing provenance
data during exploration and then apply such provenance
data to presentation [118]. However, visual encodings opti-
mized for exploratory purposes are not necessarily optimal
for communication. Chen et al. propose a different approach
by inserting a story synthesis phase between analytics and
storytelling. It enables users to arrange and aggregate story
slices and add annotations and visual linkings [119]. In
journalism, the data exploration and insights finding can
be messy narratives may come from different views and
datasets across different visual analytic systems, and their
story authoring often demands originality and creativity.
It is worth exploring how to support such demands while
respecting the underlying data and relationship.
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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Subtopic 1.3: Enable, facilitate, and understand data-
driven commenting (Public Forum)
Agenda: Enable audiences of data story/dashboard to
comment with data-driven insights (i.e., their find-
ings/interpretations from interacting with dashboards),
design interactive mechanisms to facilitate such data-
driven dialogues among commenters, and study its in-
fluence on data-driven public communication
Literature: [14], [99], [117], [121], [122], [123]
While data-driven storytelling combines data explo-
ration with data communication through interactivity and
is equipped with novel techniques to keep audiences en-
gaged, we have yet to see platforms that facilitate audiences
participating in data-driven dialogues. By data-driven di-
alogues, we mean activities in which audiences comment
and interact with others using their own analytic insights.
Commenting is among the most used feature to foster user
input in online journalism, especially when it comes to
controversial topics such as politics [123]. Surveys [122]
show people comment on the news to express emotion or
opinions, add information or correct misinformation, par-
ticipate in debates, etc. In a traditional textual environment,
it is a relatively balanced communication for audiences to
respond with texts. When arguments are accompanied by
visual support, researchers have observed that comments
tend to be more analogous and supportive [117]. Although
it could be a sign of improved credibility, it can also
be interpreted as suppression of counterarguments. Data
visualization’s effect on audiences’ commenting behavior
remains understudied. How can we facilitate better data-
driven conversational experiences for audiences? The Mar-
tini Glass structure suggested by Segel and Heer [14] can
potentially prompt readers to explore, but readers still lack
an interactive mechanism to facilitate effective exchanges
of the analytic insights they discover. Viegas et al. [99]
discusses ManyEyes social features it not only allowed
users to comment underneath visualizations and datasets
but also enabled them to “snapshot” a visualization’s state
or include graphical annotations into their comments. Hull-
man et al.’s study on visualization blog comments discusses
the possibility of more sophisticated commenting interfaces
that support functionalities such as comment-presentation
linking [121]. We envision further research in this direction,
including exploring the design space for data commenting,
developing prototypes to enable data commenting activities
(e.g., linking “states” of a COVID dashboard to readers’
comments and allowing others to access quickly), and field
deployment to investigate their impact on public data dis-
cussions using such prototypes.
5.2 Understand Visualization Dashboards on News
Websites
While the previous subsection primarily focuses on the
technical perspective, this subsection places emphasis on
empirical knowledge concerning current practices and de-
sign knowledge aimed at improving existing technology,
specifically regarding visualization dashboards.
Despite the widespread presence of visualization dash-
boards across different industries, it is only recently that
they have begun to enter our daily lives. An unprece-
dented amount of visualization dashboards have been pro-
duced to combat the COVID-19 pandemic [124]. They have
played a vital role in assisting both top-down policymakers’
decision-making and informing the public about the situa-
tion from local to global levels, thus altering our awareness
and behaviors [13]. The importance of well-designed data
dashboards cannot be understated in this misinformation-
flooded era. News organizations, represented by the New
York Times, have contributed a significant portion of these
dashboards. Such visualization dashboards as news appli-
cations have become a predominant form of data journal-
ism [30], [55], [56].
Subtopic 2.1: Investigate how visualization dashboards
are created and used as a form of journalism (Analyzer,
Communicator)
Agenda: Empirically investigate how visualization dash-
boards are designed and implemented by news organiza-
tions and used by news readers and other stakeholders
(e.g., participatory journalists)
Literature: [13], [125], [126], [127]
Visualization researchers have recently studied how
dashboards are designed and used in the wild. Through
their case survey and literature review, Sarikaya et al. [125]
characterize and organize 15 factors into categories, in-
cluding purpose, audience, visual & interactive features,
and data semantics. They also advocate for visualization
researchers to engage with users in the wild and sys-
tematically study dashboard design and implementation.
Zhang et al. answer their calling by interviewing people
involved in the COVID-19 dashboard design & creation
process [13], including those from news organizations. Their
study highlights the entangled relationships among ac-
tors and suggests a sociotechnical perspective for future
investigation. Bach et al.’s recent design workshop offers
insights on dashboard design patterns and tradeoffs [127].
Such empirical studies can deepen our understanding of
design knowledge, stakeholders’ practical pain points, the
relationships among actors and technologies, and the role
that visualization dashboards play in a larger social context.
An operationalizable contribution is design guidelines
for public-facing visualization dashboards. Design guide-
lines for other users (e.g., analysts, domain experts) are
often crisp and tailored for a set of design requirements.
Design practices often require designers to “know your au-
diences”. When it comes to the public, audiences are so
diverse that designs are often geared toward the majority,
marginalizing other groups. How could interactive visu-
alization dashboards benefit more groups? We envision
more flexible, customizable, and adaptable design guide-
lines as Sarikaya et al. advocate [125]. Also, evaluating
visualization artifacts has already been a big challenge
in the visualization community [128], let alone evaluating
tools that are public-facing. In-the-wild, long-term evalu-
ation methods like the multi-dimensional in-depth, long-term
case studies (MILC) method [129] proposed by Shneiderman
and Plaisant could offer inspiration for future research to
develop public-facing evaluation methods.
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Subtopic 2.2: Explore the design space of engaging casual
audiences into analytical tasks (Analyzer)
Agenda: Use design studies as knowledge production
devices to understand how different visual/interaction
designs impact different audiences’ transition from casual
engagements into more analytical ones
Literature: [130], [131], [132], [133]
Visualization dashboards are at their best when the
power of interactivity can be fully wielded by their users.
But as noted by multiple studies [126], [134], the usage of
interactivity in online news is an “uncomfortable myth” [134].
Then how can visualization and interaction design better
engage and encourage audiences to interact and conduct
analysis? A suitable approach to investigate such topics
is through design study. In visualization research, a design
study investigates a real-world problem and its context,
design and develops a visualization system solution, vali-
dates design choices, and reflect on lessons learned [130],
[131]. The resulting artifacts are usually visualization pro-
totypes and design guidelines, which can be valuable for
data journalists to borrow and follow. There is a disparity,
however — data journalists desire to inform the public who
have greater variance in data literacy, prior knowledge,
and intention, while visualization design researchers have
specific domain experts in mind and would prefer crisp
domain tasks [135]. Meyer and Dykes’s renewed discus-
sion on design study draws on Research through Design
(RtD) [132], [133] and emphasizes reflectively generating
knowledge about the complex, messy, nuanced, and evolving
relationships of people with data and technology through visu-
alization design [131]. For design studies to benefit public-
facing data journalism, visualization design researchers can
explore visual/interaction design space with our ecologi-
cal model (Figure 3) in mind, which means considering
recipient’s’ encounters with dashboards casual and incen-
tivize their analytical engagement and possible transition
into creators’ role. Such design studies should shy away
from a full “utilitarian perspective” that aims at “the most
effective” solutions and “in-lab” user studies that often omit
the connections among different stakeholders. Instead, de-
sign researchers can intentionally produce insights through
different visual/interaction designs and gather long-term
user feedback from different social groups.
5.3 Integrate Interactive Visualization with New Media
Subtopic 3.1: Facilitate data video authoring and aug-
menting (Facilitator)
Agenda: Facilitate data video authoring and presentation
and augment video with visualization and novel interac-
tions
Literature: [14], [136], [137], [138], [139], [140]
The importance of video content in journalism can-
not be overstated. The emergence of short-form video
platforms (e.g., TikTok) has taken its importance to an
unprecedented level [140]. Segel and Heer [14] include
Film/Video/Animation as a basic narrative visualization
genre. Nevertheless, integrating data visualization into
videos requires tremendous effort and skills, along with
a plethora of tools [136]. Visualization researchers have
made attempts to address these challenges. One route is to
develop tools that directly facilitate data video authoring.
For example, Amini et al. developed DataClips [136] to
consolidate these creation tasks into one tool, significantly
reducing the production efforts and time while maintaining
similar quality similar to professional editing tools. Recently,
Shin et al. developed Roslingifier [138] to semi-automate
the data presentation. Another route is to augment existing
video footage with visualization. It is commonly seen in
content-focused journalism domains, such as sports. Sports
journalists and analysts frequently need to employ analytics
to tell a narrative [117]. Besides the efforts from commercial
companies, Chen et al. have done extensive work on this
front. They developed VisCommentator [137] and Sporthe-
sia [139]. The former takes raw video footage and extracts
data via an ML model, allowing users to interact with ob-
jects in the video and select the data to visualize. The latter
further eases the creation process by supporting natural lan-
guage control by leveraging NLP techniques. Furthermore,
Lin et al. recently developed Omnioculars [141], which
embedded interactive visualizations into in-game video,
extending visualization’s potential to augment broadcasting
and situated in-game journalistic analysis.
Subtopic 3.2: Visualization for live streaming (Facilitator,
Communicator)
Agenda: Design expressive spectator interfaces for mobile
device interaction and study their adoption and effects
Literature: [142], [142], [143], [144], [145]
Live streaming has recently emerged as a popular form
of social interaction, and many journalists have adopted it
as a way to do journalism. Visualization in a broadcasting
setting is not a new thing news TV channels have been
using interactive dashboards on multiple occasions (e.g.,
CNN’s Magical Wall, ESPN). Online streaming allows more
journalists to participate in this type of communication,
which Wattenberg describes as one person will be active,
controlling the input, while others in the group will act as specta-
tors [142]. He calls such interactive visualizations the “Ex-
pressive Spectator Interface.” Online streaming revitalizes
an old design challenge for HCI — “designing the spectator
experience” [146]. From a sole interface design perspective,
as online streaming tends to happen on the mobile end, we
speculate that it poses new research questions that align well
with recent visualization research concerning visualization
on mobile devices and multi-modal interactions for data
visualization [143], [144], [145], though audiences’ willing-
ness to spectate and journalists’ needs for such interfaces are
subject to future investigation.
5.4 Support Journalists’ Analytical Tasks through Vi-
sual Analytics
Subtopic 4.1: Support data-driven narrative construction
(Analyzer)
Agenda: Design and develop domain-driven visual an-
alytic systems that help journalists uncover novel data-
driven storylines from a growing amount of domain data
and analytics
Supporting Literature: [117], [147]
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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As more domains are becoming data-driven, journalists’
demand for analytics and visualization tools to uncover
novel narratives has grown in parallel, particularly within
areas that have an abundance of data and analytics, such
as finance and sports. For example, sports journalists in-
creasingly rely on analytics to construct novel and credible
narratives, and interactive visualization offers a way to
rapidly obtain analytical insights and engaging visuals for
their storytelling [117]. Zhi et al. [147] investigated how
sports writers and fans can both benefit from interactive
visualization to access statistics. Fu and Stasko [117] took
consideration of the shifts triggered by sports analytics
and developed two visualization systems tailored for bas-
ketball journalists/analysts to explore data and obtain in-
sights [117]. Assisting them in such endeavors would re-
quire a deeper understanding of both journalists’ narrative
construction, workflow, and the characteristics of domain
data/analytics. As such, contributions of such design study
include formative investigation to identify domain jour-
nalists’ analytical tasks, visualization prototypes that lead
to rapid analysis, novel/interesting storylines, and visual
representations that can be incorporated into stories and
insights about how such interventions impact journalists’
and their audiences.
Subtopic 4.2: Support document investigation through
visual analytics (Analyzer)
Agenda: Simplify the use of such systems; reduce data
wrangling; support flexible data import, integrate new
text mining techniques; support evidence marshaling
Literature: [97], [98], [148], [149], [150]
From early CAR to recent computational journalism,
investigative journalists employ various computational ap-
proaches to perform their watchdog duties. Assisting them
in making sense of quantitative data can certainly lead to
valuable storylines, as we discussed, but here we intend
to highlight another approach that is in line with tradi-
tional investigative tasks finding leads for stories from
large numbers of textual documents. Modern-day text min-
ing/analytics technologies, notably natural language pro-
cessing (NLP), have allowed the transformation of unstruc-
tured textual data into more structured, analyzable data,
extending visualization’s capability to represent qualitative
information and help make sense of it. A cluster of visual-
ization research has been dedicated to mining insights from
a large corpus. Jigsaw [97], [148], for example, leverages
visual analytics to aid investigative analysis. Jigsaw features
flexible data imports, entity identification, and a set of inter-
connected views to help analysts identify the connections
between entities in documents in order to glean a compre-
hensive understanding of the themes and patterns across
textual documents. Brehmer et al. specifically targeted in-
vestigative journalists and closely collaborated with them.
They developed multiple versions of Overview [98], which
led to nine published investigative stories. They studied
how journalists adopt Overview and suggested simplifying
the use and reducing data wrangling. For future research,
G
¨
org et al. [149] reflect that data ingestion that supports
“flexible data import is challenging but vital” as documents to
investigate are in a great variety of sources, forms, and for-
mats. Another key is to integrate more advanced text mining
techniques that can improve entity identification [149]. Evi-
dence marshaling, a capability to support analysts in draw-
ing connections and adding annotations, was also proposed
to be included as an important feature [149], [150].
5.5 Visualization for Journalistic Translucence
In their prescient paper on designing digital systems that
support social processes, Erickson and Kellogg propose
three building blocks of social interaction: visibility, aware-
ness, and accountability [151]. They employ visualization
to address “blindness” in their demonstration application
Babble”. Many online communities or conversations now
have built-in socially translucent structures or functional-
ities. Our digital blindness has been reduced to a certain
degree when it comes to awareness of other people’s pres-
ence and status. When it comes to our information ingestion,
however, our vision has seemingly become narrowed in
today’s algorithm-pervasive information spaces, our vision
often zooms in on what is on our screens and tends to
lose track of information beyond that, which can impose
walls between algorithmically classified groups and lead to
further social fragmentation. As demonstrated by Babble”,
visualization representations have great value in reification.
In other words, they make abstract information perceivable,
interpretable, and even actionable. Inspired by their work,
we propose to appropriate the concept of social translucence
in journalistic contexts — journalistic translucence that con-
sists of three aspects: making news personalization “visi-
ble”, raising awareness of journalistic content consump-
tion, and holding media content production accountable.
Achieving these goals, however, requires more sophisticated
computational support. Fortunately, the abundant data and
information extraction technologies (e.g., NLP, ML), along
with advances in information visualization, particularly in-
teractivity, provide the repertoire to make it possible.
Subtopic 5.1: Make news personalization “visible” (Au-
ditor)
Agenda: Leverage interactive visualization/visual ana-
lytics to interpret news personalization algorithms and
reverse engineer them to inform the public
Literature: [62], [152], [153], [154], [155], [156], [157], [158]
HCI researchers have explored the use of visualization to
raise news personalization awareness. For example, Eslami
et al. developed FeedVis [152], which employs straightfor-
ward views to demonstrate the proportion of news that
is shown or hidden by the algorithm and allows users to
adjust the algorithms based on authorship and story con-
tent. Their study also points out four paths to news aware-
ness. Additionally, leveraging visualization to enhance the
interpretability of AI and machine learning models has
been a recent trend and promising research area [153],
[154], [155], [156]. Inspired by this cluster of research, we
envision that visualization research can contribute in two
ways: within news organizations, where news personal-
ization algorithms are available to be directly audited, vi-
sualization can be applied to interpret and explain such
algorithms to decision-makers; for news audiences who do
not have access to such algorithms, visualization can help
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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with “reverse-engineering” algorithms and informing the
public [62], [157].
Subtopic 5.2: Raise news consumption awareness (Audi-
tor)
Agenda: Combine personal data tracking, text analytics,
and visualization to raise individual readers’ awareness
of news consumption; potentially employ personal data
art to motivate them to explore and share with others
Literature: [159], [160]
People’s personal data has been used by media platforms
for personalization, but users themselves have very limited
access to their own data, let alone the tools to support
them in making sense of it. Recent trends, nevertheless, are
revealing people’s desire to better understand themselves
using the collection of personal data. Tech companies have
attempted to visualize users’ personal data for marketing
purposes. For instance, Wrapped, Spotify’s year-end visual
review of users’ music tastes, went viral and won Webby
People’s Voice Award for Best Data Visualization [161]. The
New York Times recently launched a similar endeavor
Story Portrait, which generates a personalized and shareable
visual portrait of the headlines of the stories from the past
two years, with colors representing different sections [162].
The capabilities to track and collect personal news intake
can provide opportunities for readers to be aware of the
news they consume. Researchers in other areas have ex-
plored providing feedback to news readers about their
news consumption. For example, Munson et al. recently
developed Balancer [163], a Chrome Extension featuring
icons with minimum visualization. Their deployment study
suggests that showing aggregated feedback can nudge some
readers toward more balanced news consumption. As we
discussed, visualization researchers have created tools to
assist corpora analysis that could provide news readers with
more sophisticated insights beyond mere political stances.
We envision that to graft these techniques and tools onto a
journalistic setting requires further examination of how to
incite audiences’ curiosity and prompt them to explore, as
audiences do not have the motivation to expose themselves
to information outside their ideological niche. Considering
people’s favor for the aestheticization of personal data [159],
[160], data art could potentially engage audiences and pro-
vide motivations for them to explore and share.
Subtopic 5.3: Hold news production accountable (Audi-
tor)
Agenda: Leverage text analytics and interactive visual-
ization and adopt action research methodology to help
activists/advocacy hold news organization accountable
Literature: [164], [165], [166], [167], [168], [169]
Similar visual text analytics tools/techniques can be
used to examine individual/institutional news production,
potentially by social activists. News practitioners and ac-
tivists have made efforts and provided inspiration. For in-
stance, the Pudding recently published a visual essay titled
When Women Make Headlines [168]. They used data scraped
from Google News and leveraged gendered language, bias
calculation, theme dictionaries, and polarity analysis to
foreground the misrepresentation of women in the news.
Similarly, VisualizeNews combined NLP and intriguing nar-
rative visualizations to examine the press coverage of the
2019 Indian general election [166]. More serious democracy
activist efforts like Hamilton 2.0 Dashboard [167] have em-
ployed interactive visualization dashboards to demonstrate
analysis of narratives and topics promoted by state-funded
media. Research in this direction can potentially adopt ac-
tion research [164], [165], [169] as a methodology.
5.6 Combat Misinformation
“Fake news” is accelerating and impairing people’s judg-
ment of information, worsening the information environ-
ment for journalism, which is already suffering from trust
crises. Combating the scourge of misinformation is essential
to our society and has attracted serious attention from vari-
ous disciplines. Additionally, large language models have
the capacity to generate more persuasive misinformation
on a massive scale [170], highlighting the urgent need for
counteracting technologies.
Visualizations have been used to present the characteris-
tics of misinformation. For example, FakeNewsTracker [171]
employs visualization interfaces to demonstrate the fake
news they detect using machine learning models. Instead of
adding to this thread, here we identify three other directions
where visualization research can perform a larger role in
combating misinformation.
Subtopic 6.1: Visualization for investigating and commu-
nicating misinformation (Analyzer, Communicator)
Agenda: Leverage visual analytics to surface the trust-
worthiness and biases of news sources/content in order
to facilitate decision-making and fact-checking endeavors
and mitigate confirmation biases; employ narrative visu-
alization to communicate fact-checking results in a more
convincing and digestible way
Literature: [45], [80], [172], [173], [174], [175], [176], [177],
[178]
Fact-checking has a long history as a standard and es-
sential routine in the newsroom [45]. With the explosion of
misinformation, fact-checking has become increasingly chal-
lenging, yet critically important [80]. We identify two angles
to approach this matter with the assistance of visualization.
The first angle concerns the analysis of fact-checking. Fact-
checkers often need to examine different attributes of the
information [45], including entities, linguistic features (e.g.,
sentiment), and metadata (e.g., sources, timestamps). Visu-
alization possesses the capability to represent such high-
dimensional information and relationships among various
entities. Coupled with interactivity and analytics, it could
enable fact-checkers to label sources, claims, and relation-
ships more efficiently and with fewer biases. Visualization
researchers [172], [173], [174] have contributed tools and
designs to help investigate misinformation. Future research
can investigate fact-checking workflow and develop an-
alytic tools that empower them to rapidly check claims
against other statements/facts or quantitative data and
assess their credibility. In addition, techniques [177], [178]
used to surface analysts’ biases and promote self-awareness
can also be integrated into such fact-checking tools.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 07:43:48 UTC from IEEE Xplore. Restrictions apply.
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The second angle attends to the challenge of communi-
cating results. Fact-checking websites adopt various ways
to present their verdict and analysis on claims/rumors,
including visual representations (e.g., PolitiFact’s Truth-O-
Meter [179]). Despite their efforts, delivering such verdicts
can be laborious and sometimes counterproductive if done
in a threatening manner [175], [180]. There are also ini-
tiatives aiming to make fact-checking more visible [45].
We speculate narrative visualizations, with their capabil-
ities to deliver sophisticated information in an engaging
manner, could support communicating fact-checking ver-
dict/analysis in a more digestible and transparent way
(e.g., demonstrating a verdict’s relationships with mul-
tiple sources supporting/refuting it). Another promising
approach is to employ visualizations to effectively com-
municate the verdicts on data-driven/statistical claims, as
visualization can potentially provide context for the data
claims and help audiences to comprehend the verdicts. Fu-
ture research can explore the design space and evaluate its
influence on audiences’ reception of fact-checking verdicts,
regarding aspects such as engagement and persuasiveness.
Subtopic 6.2: Detect visual deception (Auditor)
Agenda: Develop end-user tools (e.g., browser extensions)
that can detect visual deception/distortion and alert news
readers with annotations and visual augmentations
Literature: [181], [182], [183], [184]
While visualizations possess communicative and even
persuasive power, they can also be used for deception [181].
There are countless notorious cases from marketing com-
panies, news organizations, and government institutions
even the White House posted a problematic chart on Twitter
recently [185]. Regardless of their intentions, it becomes
increasingly important to alert and inform the public about
potential visual deception. Visualization design guidelines
are often about “how NOT to lie with charts” [186], [187]
but in reality, people have so many incentives not to follow
them. More researchers have begun to focus on detecting
these “graphical lies.” Pandey et al. summarize popular
distortion techniques and their effect. They classify de-
ceptive visualizations into two groups: message exaggera-
tion/understatement and message reversal [181]. Hopkins
et al. create VisualLint to alert visualization designers about
such errors. McNutt et al. introduces a concept of “visual-
ization mirages” and suggests alerting readers to potential
issues [183]. Fan et al. developed a Chrome Extension that
automatically reads and annotates potential deceptions in
line charts, making such techniques more easily accessible
for readers [182]. This opens up future research possibilities
to broaden the chart types and increase accessibility.
Subtopic 6.3: Detect text-visualization misalignment
(Auditor)
Agenda: Develop end-user tools (e.g., browser extensions)
that can point out the misalignment between visualization
and text (e.g., titles, annotations)
Literature: [188], [189], [190]
Deception can also occur when visualizations do not
match the textual descriptions or claims in titles or anno-
tations. Borkin et al.’s experiment based on eye-tracking
suggests that titles and text can greatly impact how people
recall the gist of visualization [188]. Kong et al.’s studies
demonstrate that slanted titles and misaligned annotations
can mislead audiences’ interpretation of visualizations and
have more persistent impacts on recalling than visualiza-
tions do [189], [190]. They also suggest using algorithms
and NLP methods to combat text-visualization misalign-
ment and tools to foreground such misalignment and raise
readers’ awareness [190]. We anticipate that web browser
plugins capable of detecting and notifying readers of such
misalignment would come in handy, although it presents
technical challenges to improving the accuracy, and its im-
pact on reader experience needs further examination.
5.7 Automated Visual Stories and Insights (Automator,
Communicator)
Subtopic 7.1: Automate visual data story creation (Au-
tomator)
Agenda: Enable (semi-)automated generation of more so-
phisticated and engaging data-driven storytelling and un-
derstand readers’ perception of such automated content
Literature: [76], [77], [191], [192], [193], [194], [195]
Robot-generated news has its advantages when it comes
to speed, scalability, and even objectivity, but it often
lacks sophisticated narratives and has been deemed bor-
ing and technical [77]. HCI researchers have explored au-
tomated news content beyond just text. Oh et al. devel-
oped a system NewsRobot [192] that automatically gen-
erates different types (general/personalized) and styles
(text/image/sound) of news content using sports data,
and they used generated news as a probe to understand
audiences’ perception of different combinations of auto-
mated news. Visualization researchers have studied it from
a broader perspective auto-insights, defined as “data
observations revealed by automation” by Law et al. [191].
Their review [191] found that only a few studies were
dedicated to automated data-driven storytelling. Systems
like DataShot [193], Temporal [194], and Calliope [195] are
pushing towards more sophisticated and engaging auto-
mated visual data stories while supporting human editorial
intervention. This direction still imposes great technical
challenges (e.g., extracting data insights, generating proper
visualization, linking texts to charts, supporting sophisti-
cated techniques like scrollytelling) and lacks design impli-
cations and understanding of audience perceptions.
Subtopic 7.2: Augment interactive visualization with
automated insights and guidance (Automator)
Agenda: Provide auto-generated insights and guidance
based on users’ interactions to help ordinary audiences
interpret data and visualization
Literature: [196]
With visualization dashboards permeating various areas
in journalism and reaching broader audiences, the pub-
lic’s need for assistance in interpreting these visualizations
and extracting insights also grows. Driven by commercial
companies like Automated Insights, multiple auto-insight
systems have emerged. Such systems use statistical models
to infer potentially interesting/relevant facts in the data
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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JOURNAL OF L
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and generate natural language to communicate them [196].
Srinivasan et al. discuss such template-based natural lan-
guage generation systems that assist users in visualization
interpretation. They introduce Voder, a system that automat-
ically generates data facts according to visualization and
links data facts to the visualization through visual embel-
lishments (i.e., opacity, regression lines) [196]. As they note,
such tools are still in their infancy and how to contextualize
them in journalism to benefit the audiences is still a target
for future investigation. Nevertheless, such systems offer the
potential to guide ordinary people in data exploration and
interpretation and ultimately increase their data literacy.
5.8 Topic Connections and Other topics
It is worth noting that not all of the topics we list are
independent. The ecological model (Figure 3) we propose
could help demonstrate their intricate relationships. Such
relationships can be synergistic e.g., the relationship
between investigating misinformation and communicating
verdicts, two directions mentioned in Subtopic 6.1, can be
strengthened by a facilitating tool advocated in Subtopic
1.2. The relationships can also be antagonistic e.g., au-
tomated visual storytelling technology discussed in Subtopic
7.1 could potentially weaken human-centric approaches as
it bypasses the topics associated with Analyzer (e.g., Subtopic
4.1 & 4.2) and Facilitator (e.g., Subtopic 1.1) and generates
Communicator with minimum human intervention.
In addition, our proposed set of topics is not intended
to be exhaustive. Our goal is to open up the possibilities
for this cluster of research. Other topics, such as playable
visualization [197], [198], immersive data-driven stories [199],
and archiving data-driven stories and apps [200], [201], [202],
[203], [204] are worthy topics that have been studied by
researchers and likely have applications in journalism.
6 REFLECTION
Although the main thrust of this paper is to explore the
broader role of visualization in journalism and thus provide
future agendas for visualization research to better assist con-
temporary journalism, our review of the literature on both
fronts indicates shared interests between the two research
areas and potential benefits for visualization research.
As Meijer [66] notes, journalism researchers have turned
to “news experience” and considered an “expressive” angle
to study journalism. Scholars have employed new concepts
and explanatory frames, including HCI studies and infor-
mation studies, to analyze topics like people’s experience
of information and how computing-mediated interactive
experience can alter audiences’ preferences, perceptions,
and experiences of news [66]. Such goals align well with the
visualization/HCI research, though they are likely to have
different foci. We advocate closer collaborations between
these fields. It can lead to a more balanced and heteroge-
neous perspective visualization/HCI researchers tend to
lean towards a technological or cognitive science one while
traditional journalism study is dominated by a sociological
perspective [205]. For example, both sides have shown inter-
est in data literacy of the public visualization researchers
may have a stronger interest in how it affects people’s
interpretation of different visual representations [206], how
to better design visuals and interactions for the public
with varying levels of data literacy, and how to improve
people’s data/visualization literacy [207], while journalism
scholars may focus on how data/visualization literacy im-
pacts public discourse and its implications for journalistic
products. Visualization research can provide devices for
data gathering and cognitive bases to explain high-level
social phenomena, while journalism study offers a holistic
social context to situate the generated knowledge and make
such knowledge more useful and actionable.
In addition to collaborating with journalism scholars,
visualization research can also benefit from working with
journalism practitioners. Apart from the fact that they are
top-tier stakeholders and hence the necessity to understand
their workflow and needs, another practical benefit is that
journalists can potentially increase the visibility and appre-
ciation of visualization outside the core visualization com-
munity, which has been advocated by some visualization
researchers [208]. The value of visualization is challenging
to perceive without “seeing it” or “interacting with it”,
especially for audiences less familiar with the affordance of
interactive visualizations and/or with limited data/visual
literacy. Journalists’ platforms, whether institutional or in-
dividual, can provide vehicles for visualizations to reach
broader audiences, increase the visibility of visualization,
and potentially improve the data and visual literacy of the
masses. Journalists are natural information transmitters
assisting journalism in a broader way offers visualization
research a chance to increase its real-world visibility not
only putting enthralling graphics in front of people but also
demonstrating to them how they can converse with their
surrounding information in an effective yet joyful way.
7 CONCLUSION
Journalism and visualization have an intertwined relation-
ship, one that is often overshadowed by visualization’s
capability in communication. Indeed, visualization excels at
aiding data-driven storytelling, but it can also be instru-
mental to journalists and news audiences in many other
ways, with its own set of aesthetic and functional values. In
this study, we took a step back and examined journalism’s
digital metamorphosis to identify the emerging challenges
and their implications, as well as journalism’s computa-
tional exploration to address such challenges and shifts.
Based on our findings, we identified computing’s six roles in
contemporary journalism, and their corresponding focuses.
Next, we revisited discourse on the fundamental values of
visualization and proposed a set of propositions about how
visualization can support or be supported by computing’s
six roles. By situating these roles and propositions in an
ecological model, we ultimately surfaced a range of research
topics and respective agendas. Through this paper, we hope
to shed light upon the shifts in contemporary journalism
and their implications for visualization research and inspire
broader research exploration at the intersection of these two
dynamic fields.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 07:43:48 UTC from IEEE Xplore. Restrictions apply.
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REFERENCES
[1] J. J. Otten, K. Cheng, and A. Drewnowski, “Infographics and
public policy: Using data visualization to convey complex in-
formation,” Health Affairs, vol. 34, no. 11, pp. 1901–1907, 2015.
[2] M. Bostock, V. Ogievetsky, and J. Heer, “D³ data-driven docu-
ments,” IEEE Transactions on Visualization and Computer Graphics,
vol. 17, no. 12, pp. 2301–2309, 2011.
[3] A. Bruns, “Gatekeeping, gatewatching, real-time feedback: new
challenges for journalism,” Brazilian journalism research, vol. 7,
no. 2, pp. 117–136, 2011.
[4] S. C. Lewis, K. Kaufhold, and D. L. Lasorsa, “Thinking about
citizen journalism,” Journalism Practice, vol. 4, no. 2, pp. 163–179,
2010.
[5] B. Anderson, Imagined communities: Reflections on the origin and
spread of nationalism. Verso books, 2006.
[6] K. Wahl-Jorgensen and T. Hanitzsch, “Journalism studies: Devel-
opments, challenges, and future directions,” in The Handbook of
Journalism Studies, 2nd ed., K. Wahl-Jorgensen and T. Hanitzsch,
Eds. Routledge, 2019, pp. 3–20.
[7] B. McNair, “Journalism and democracy,” in The Handbook of
Journalism Studies, 1st ed. Routledge, Jan 2009, pp. 257–269.
[8] C. Peters and M. Broersma, Rethinking Journalism: Trust and Par-
ticipation in a Transformed News Landscape. Routledge, 2013.
[9] J. Pavlik, Journalism and New Media. Columbia University Press,
2001.
[10] A. V. Dalen, “Journalism, trust, and credibility,” in The Handbook
of Journalism Studies, 2nd ed., K. Wahl-Jorgensen and T. Han-
itzsch, Eds. Routledge, 2019, pp. 356–371.
[11] J. M. Ladd, Why Americans Hate the Media and How It Matters.
Princeton University Press, 2012.
[12] P. Meyer, Precision Journalism: A Reporters Introduction to Social
Science Methods. Rowman & Littlefield Publishers, 2002.
[13] Y. Zhang, Y. Sun, J. D. Gaggiano, N. Kumar, C. Andris, and A. G.
Parker, “Visualization design practices in a crisis: Behind the
scenes with covid-19 dashboard creators,” IEEE Transactions on
Visualization and Computer Graphics, vol. 29, no. 1, pp. 1037–1047,
2023.
[14] E. Segel and J. Heer, “Narrative visualization: Telling stories with
data,” IEEE Transactions on Visualization and Computer Graphics,
vol. 16, no. 6, pp. 1139–1148, 2010.
[15] J. Hullman and N. Diakopoulos, “Visualization rhetoric: Framing
effects in narrative visualization,” IEEE Transactions on Visualiza-
tion and Computer Graphics, vol. 17, no. 12, pp. 2231–2240, 2011.
[16] N. Riche, C. Hurter, N. Diakopoulos, and S. Carpendale, Data-
Driven Storytelling, ser. AK Peters Visualization Series. CRC
Press, 2018.
[17] R. Kosara and J. Mackinlay, “Storytelling: The next step for
visualization,” Computer, vol. 46, no. 5, pp. 44–50, 2013.
[18] B. Lee, N. H. Riche, P. Isenberg, and S. Carpendale, “More than
telling a story: Transforming data into visually shared stories,”
IEEE Computer Graphics and Applications, vol. 35, no. 5, pp. 84–90,
2015.
[19] M. T. Pham, A. Raji
´
c, J. D. Greig, J. M. Sargeant, A. Papadopou-
los, and S. A. Mcewen, “A scoping review of scoping reviews:
advancing the approach and enhancing the consistency,” Research
Synthesis Methods, vol. 5, no. 4, pp. 371–385, 2014.
[20] C. I. of Health Research, “A guide to knowledge synthesis - cihr,”
https://cihr-irsc.gc.ca/e/41382.html, Cihr-irsc.gc.ca, 2010.
[21] D. Long and B. Magerko, “What is ai literacy? competencies and
design considerations,” in Proceedings of the 2020 CHI Conference
on Human Factors in Computing Systems, ser. CHI ’20. ACM, 2020,
pp. 1–16.
[22] V. Herdel, L. J. Yamin, and J. R. Cauchard, “Above and beyond:
A scoping review of domains and applications for human-drone
interaction,” in Proceedings of the 2022 CHI Conference on Human
Factors in Computing Systems, ser. CHI ’22. ACM, 2022.
[23] H. Arksey and L. O’Malley, “Scoping studies: towards a
methodological framework,” International Journal of Social Re-
search Methodology, vol. 8, no. 1, pp. 19–32, 2005.
[24] K. Wahl-Jorgensen and T. Hanitzsch, The Handbook of Journalism
Studies, 1st ed. Routledge, 2009.
[25] C. Wohlin, “Guidelines for snowballing in systematic literature
studies and a replication in software engineering,” in Proceedings
of the 18th International Conference on Evaluation and Assessment in
Software Engineering, ser. EASE ’14. ACM, 2014.
[26] B. Scott, “A contemporary history of digital journalism,” Televi-
sion & New Media, vol. 6, no. 1, pp. 89–126, 2005.
[27] D. Domingo, T. Quandt, A. Heinonen, S. Paulussen, J. B. Singer,
and M. Vujnovic, “Participatory journalism practices in the media
and beyond,” Journalism Practice, vol. 2, no. 3, pp. 326–342, 2008.
[28] J. V
´
azquez-Herrero, X. L
´
opez-Garc
´
ıa, and F. Irigaray, The
Technology-Led Narrative Turn. Springer International Publishing,
2020, pp. 29–40.
[29] D. S. Chung and C. Y. Yoo, “Audience motivations for using
interactive features: Distinguishing use of different types of in-
teractivity on an online newspaper,” Mass Communication and
Society, vol. 11, no. 4, pp. 375–397, 2008.
[30] N. Usher, Interactive journalism: Hackers, data, and code. University
of Illinois Press, 2016.
[31] S. Jones and A. Bruns, Gatewatching: Collaborative Online News
Production, ser. Digital formations. P. Lang, 2005.
[32] M. Carlson and S. C. Lewis, “Boundary work,” in The Handbook of
Journalism Studies, 2nd ed., K. Wahl-Jorgensen and T. Hanitzsch,
Eds. Routledge, 2019, pp. 123–135.
[33] P. J. Shoemaker, T. P. Vos, and S. D. Reese, “Journalists as gate-
keepers,” in The Handbook of Journalism Studies, 2nd ed., K. Wahl-
Jorgensen and T. Hanitzsch, Eds. Routledge, 2019.
[34] J. Singer, D. Domingo, A. Heinonen, A. Hermida, S. Paulussen,
T. Quandt, Z. Reich, and M. Vujnovic, Participatory Journalism:
Guarding Open Gates at Online Newspapers. Wiley, 2011.
[35] R. Zamith and S. C. Lewis, “From public spaces to public sphere,”
Digital Journalism, vol. 2, no. 4, pp. 558–574, 2014.
[36] E. Powers, “My news feed is filtered?” Digital Journalism, vol. 5,
no. 10, pp. 1315–1335, 2017.
[37] E. Pariser, The Filter Bubble: How the New Personalized Web Is
Changing What We Read and How We Think. Penguin Publishing
Group, 2011.
[38] B. Kovach and T. Rosenstiel, The Elements of Journalism, Revised
and Updated 4th Edition: What Newspeople Should Know and the
Public Should Expect. Crown, 2021.
[39] S. Robinson, “Traditionalists vs. convergers: Textual privilege,
boundary work, and the journalist—audience relationship in the
commenting policies of online news sites,” Convergence, vol. 16,
no. 1, pp. 125–143, 2010.
[40] A. Hermida, Mechanisms of Participation. John Wiley & Sons, Ltd,
2011, ch. 2, pp. 11–33.
[41] D. Gillmor, We the Media: Grassroots Journalism By the People, For
the People. O’Reilly Media, 2006.
[42] J. Hujanen and S. Pietik
¨
ainen, “Interactive uses of journalism:
Crossing between technological potential and young people’s
news-using practices,” New Media & Society, vol. 6, no. 3, pp.
383–401, 2004.
[43] D. S. Chung, “Interactive Features of Online Newspapers: Iden-
tifying Patterns and Predicting Use of Engaged Readers,” Journal
of Computer-Mediated Communication, vol. 13, no. 3, pp. 658–679,
04 2008.
[44] J. Pavlik, “The impact of technology on journalism,” Journalism
Studies, vol. 1, no. 2, pp. 229–237, 2000.
[45] L. Graves, Deciding What’s True: The Rise of Political Fact-Checking
in American Journalism. Columbia University Press, 2016.
[46] A. LaFrance, “The power of personalization,” https://
niemanreports.org/articles/the-power-of-personalization/, Nie-
man Reports, Feb 2019.
[47] M. Powers, “In forms that are familiar and yet-to-be invented,”
Journal of Communication Inquiry, vol. 36, no. 1, pp. 24–43, 2012.
[48] N. Thurman and S. Schifferes, “The future of personalization at
news websites,” Journalism Studies, vol. 13, no. 5-6, pp. 775–790,
2012.
[49] M. Haim, A. Graefe, and H.-B. Brosius, “Burst of the filter
bubble?” Digital Journalism, vol. 6, no. 3, pp. 330–343, 2018.
[50] S. Flaxman, S. Goel, and J. M. Rao, “Filter Bubbles, Echo Cham-
bers, and Online News Consumption,” Public Opinion Quarterly,
vol. 80, no. S1, pp. 298–320, 03 2016.
[51] M. Coddington, “Clarifying journalism’s quantitative turn,” Dig-
ital Journalism, vol. 3, no. 3, pp. 331–348, 2015.
[52] A. Gynnild, “Journalism innovation leads to innovation jour-
nalism: The impact of computational exploration on changing
mindsets,” Journalism, vol. 15, no. 6, pp. 713–730, 2014.
[53] N. Thurman, “Computational journalism,” in The Handbook of
Journalism Studies, 2nd ed., K. Wahl-Jorgensen and T. Hanitzsch,
Eds. Routledge, 2019, pp. 180–195.
[54] P. Meyer, “Defining and measuring credibility of newspapers:
Developing an index,” Journalism Quarterly, vol. 65, no. 3, pp.
567–574, 1988.
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JOURNAL OF L
A
T
E
X CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 17
[55] A. B. Howard, “The art and science of data-driven journalism,”
Academic Commons, 2014.
[56] J. Gray, L. Bounegru, and L. Chambers, The Data Journalism
Handbook: How Journalists Can Use Data to Improve the News.
O’Reilly Media, 2012.
[57] A. Holovaty, “A fundamental way newspaper sites
need to change,” http://www.holovaty.com/writing/
fundamental-change/, 2006.
[58] L. Bounegru, “Data journalism in perspective,” in The Data Jour-
nalism Handbook. O’Reilly Media, 2012.
[59] N. Diakopoulos, A Functional Roadmap for Innovation in Computa-
tional Journalism, 2011.
[60] T. Flew, C. Spurgeon, A. Daniel, and A. Swift, “The promise of
computational journalism,” Journalism Practice, vol. 6, no. 2, pp.
157–171, 2012.
[61] L. Lepp
¨
anen, H. Tuulonen, and S. Sir
´
en-Heikel, “Automated
journalism as a source of and a diagnostic device for bias in
reporting,” Media and Communication, vol. 8, no. 3, pp. 39–49,
2020.
[62] N. Diakopoulos, “Algorithmic accountability,” Digital Journalism,
vol. 3, no. 3, pp. 398–415, 2015.
[63] N. Diakopoulos and M. Koliska, “Algorithmic transparency in
the news media,” Digital Journalism, vol. 5, no. 7, pp. 809–828,
2017.
[64] P. Hammond, “From computer-assisted to data-driven: Journal-
ism and big data,” Journalism, vol. 18, no. 4, pp. 408–424, 2017.
[65] S. Parasie and E. Dagiral, “Data-driven journalism and the
public good: “Computer-assisted-reporters” and “programmer-
journalists” in Chicago,” New Media & Society, vol. 15, no. 6, pp.
853–871, 2013.
[66] I. Meijer, “Journalism, audiences, and news experience,” in The
Handbook of Journalism Studies, 2nd ed., K. Wahl-Jorgensen and
T. Hanitzsch, Eds. Routledge, 2019, pp. 389–405.
[67] G. T. G. Center, “Journalism 3g: The future of technology
in the field: A symposium on computation + journalism,”
https://web.archive.org/web/20100103125218/http://
www.computational-journalism.com/symposium/index.php,
Archive.org, 2 2008.
[68] J. T. Hamilton and F. Turner, “Accountability through algorithm:
Developing the field of computational journalism,” in Report from
the Center for Advanced Study in the Behavioral Sciences, Summer
Workshop, 2009, pp. 27–41.
[69] S. Cohen, J. T. Hamilton, and F. Turner, “Computational journal-
ism,” Communications of the ACM, vol. 54, no. 10, pp. 66–71, 2011.
[70] S. C. Lewis and O. Westlund, “Actors, actants, audiences, and
activities in cross-media news work,” Digital Journalism, vol. 3,
no. 1, pp. 19–37, 2015.
[71] M. Schudson, Why Democracies Need an Unlovable Press. Wiley,
2008.
[72] ——, “News and democratic society: past, present, and future,”
Hedgehog Review, vol. 10, no. 2, pp. 7–21, 2008.
[73] N. Usher, “Why democracies need an unlovable press,” 2009.
[74] R. Abebe, S. Barocas, J. Kleinberg, K. Levy, M. Raghavan, and
D. G. Robinson, “Roles for computing in social change,” in
Proceedings of the 2020 Conference on Fairness, Accountability, and
Transparency, ser. FAT* ’20. ACM, 2020, pp. 252–260.
[75] J. D. Wolfgang, “Pursuing the ideal: How news website com-
menting policies structure public discourse,” Digital Journalism,
vol. 4, no. 6, pp. 764–783, 2016.
[76] M. Carlson, “The robotic reporter,” Digital Journalism, vol. 3, no. 3,
pp. 416–431, 2015.
[77] A. Graefe, “Guide to automated journalism,” Columbia Journalism
Review, 2016.
[78] L. Lepp
¨
anen, M. Munezero, M. Granroth-Wilding, and H. Toivo-
nen, “Data-driven news generation for automated journalism,” in
Proceedings of the 10th International Conference on Natural Language
Generation. Association for Computational Linguistics, Sep. 2017,
pp. 188–197.
[79] B. Mittelstadt, “Automation, algorithms, and politics— auditing
for transparency in content personalization systems,” Interna-
tional Journal of Communication, vol. 10, no. 0, p. 12, 2016.
[80] D. M. J. Lazer, M. A. Baum, Y. Benkler, A. J. Berinsky, K. M.
Greenhill, F. Menczer, M. J. Metzger, B. Nyhan, G. Pennycook,
D. Rothschild, M. Schudson, S. A. Sloman, C. R. Sunstein, E. A.
Thorson, D. J. Watts, and J. L. Zittrain, “The science of fake news,”
Science, vol. 359, no. 6380, pp. 1094–1096, 2018.
[81] S. Card, S. Shneiderman, M. Card, J. Mackinlay, and B. Shneider-
man, Readings in Information Visualization: Using Vision to Think,
ser. Interactive Technologies. Elsevier Science, 1999.
[82] D. Norman, Things That Make Us Smart: Defending Human At-
tributes in the Age of the Machine. Diversion Books, 2014.
[83] J. Fekete, J. J. van Wijk, J. T. Stasko, and C. North, “The value of
information visualization,” in Information Visualization - Human-
Centered Issues and Perspectives, ser. Lecture Notes in Computer
Science, A. Kerren, J. T. Stasko, J.-D. Fekete, and C. North, Eds.
Springer, 2008, vol. 4950, pp. 1–18.
[84] J. Stasko, “Value-driven evaluation of visualizations,” in Pro-
ceedings of the Fifth Workshop on Beyond Time and Errors: Novel
Evaluation Methods for Visualization, ser. BELIV ’14. ACM, 2014,
pp. 46–53.
[85] N. Elmqvist, A. V. Moere, H.-C. Jetter, D. Cernea, H. Reiterer, and
T. Jankun-Kelly, “Fluid interaction for information visualization,”
Information Visualization, vol. 10, no. 4, pp. 327–340, 2011.
[86] Y. Wang, A. Segal, R. Klatzky, D. F. Keefe, P. Isenberg, J. Hurti-
enne, E. Hornecker, T. Dwyer, and S. Barrass, “An emotional
response to the value of visualization,” IEEE Computer Graphics
and Applications, vol. 39, no. 5, pp. 8–17, 2019.
[87] Z. Pousman, J. Stasko, and M. Mateas, “Casual information visu-
alization: Depictions of data in everyday life,” IEEE Transactions
on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1145–
1152, 2007.
[88] M. Danziger, “Information visualization for the people,” Mit.edu,
2008.
[89] D. Sprague and M. Tory, “Exploring how and why people use
visualizations in casual contexts: Modeling user goals and reg-
ulated motivations,” Information Visualization, vol. 11, no. 2, pp.
106–123, 2012.
[90] X. Lan, Y. Shi, Y. Zhang, and N. Cao, “Smile or scowl? looking at
infographic design through the affective lens,” IEEE Transactions
on Visualization and Computer Graphics, vol. 27, no. 6, pp. 2796–
2807, 2021.
[91] E. Lee-Robbins and E. Adar, “Affective learning objectives for
communicative visualizations,” IEEE Transactions on Visualization
and Computer Graphics, vol. 29, no. 1, pp. 1–11, 2023.
[92] B. Saket, A. Endert, and J. Stasko, “Beyond usability and per-
formance: A review of user experience-focused evaluations in
visualization,” in Proceedings of the Sixth Workshop on Beyond Time
and Errors on Novel Evaluation Methods for Visualization, ser. BELIV
’16. ACM, 2016, pp. 133–142.
[93] A. Cairo, The Functional Art: An introduction to information graphics
and visualization, ser. Voices That Matter. Pearson Education,
2012.
[94] S. Cohen, “Using visualizations to tell stories,” in The Data
Journalism Handbook. O’Reilly Media, 2011.
[95] S. C. Lewis and O. Westlund, “Big data and journalism,” Digital
Journalism, vol. 3, no. 3, pp. 447–466, 2015.
[96] A. Thudt, J. Walny, T. Gschwandtner, J. Dykes, and J. Stasko,
“Exploration and explanation in data-driven storytelling,” in
Data-Driven Storytelling, N. H. Riche, C. Hurter, N. Diakopoulos,
and S. Carpendale, Eds., 2018, pp. 59–83.
[97] J. Stasko, C. G
¨
org, and Z. Liu, “Jigsaw: Supporting investigative
analysis through interactive visualization,” Information Visualiza-
tion, vol. 7, no. 2, pp. 118–132, 2008.
[98] M. Brehmer, S. Ingram, J. Stray, and T. Munzner, “Overview: The
design, adoption, and analysis of a visual document mining tool
for investigative journalists,” IEEE Transactions on Visualization
and Computer Graphics, vol. 20, no. 12, pp. 2271–2280, 2014.
[99] F. B. Viegas, M. Wattenberg, F. van Ham, J. Kriss, and M. McK-
eon, “ManyEyes: a site for visualization at internet scale,” IEEE
Transactions on Visualization and Computer Graphics, vol. 13, no. 6,
pp. 1121–1128, 2007.
[100] C. Stolte, D. Tang, and P. Hanrahan, “Polaris: a system for
query, analysis, and visualization of multidimensional relational
databases,” IEEE Transactions on Visualization and Computer Graph-
ics, vol. 8, no. 1, pp. 52–65, 2002.
[101] B. Houston, “The history of data journalism: A historical take on
every critical breakthrough from the 1950s until today.”
[102] F. Chevalier, M. Tory, B. Lee, J. v. Wijk, G. Santucci, M. D
¨
ork,
and J. Hullman, “From analysis to communication,” in Data-
Driven Storytelling, N. H. Riche, C. Hurter, N. Diakopoulos, and
S. Carpendale, Eds., 2018, pp. 151–183.
[103] A. Satyanarayan, R. Russell, J. Hoffswell, and J. Heer, “Re-
active Vega: A streaming dataflow architecture for declarative
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 07:43:48 UTC from IEEE Xplore. Restrictions apply.
JOURNAL OF L
A
T
E
X CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 18
interactive visualization,” IEEE Transactions on Visualization and
Computer Graphics, vol. 22, no. 1, pp. 659–668, 2016.
[104] B. Lee, R. H. Kazi, and G. Smith, “Sketchstory: Telling more
engaging stories with data through freeform sketching,” IEEE
Transactions on Visualization and Computer Graphics, vol. 19, no. 12,
pp. 2416–2425, 2013.
[105] A. Satyanarayan and J. Heer, “Lyra: An interactive visualization
design environment,” Computer Graphics Forum, vol. 33, no. 3, pp.
351–360, 2014.
[106] Z. Liu, J. Thompson, A. Wilson, M. Dontcheva, J. Delorey,
S. Grigg, B. Kerr, and J. Stasko, “Data Illustrator: Augmenting
vector design tools with lazy data binding for expressive visu-
alization authoring,” in Proceedings of the 2018 CHI Conference on
Human Factors in Computing Systems, ser. CHI ’18. ACM, 2018,
pp. 1–13.
[107] D. Ren, B. Lee, and M. Brehmer, “Charticulator: Interactive
construction of bespoke chart layouts,” IEEE Transactions on
Visualization and Computer Graphics, vol. 25, no. 1, pp. 789–799,
2019.
[108] J. Zong, D. Barnwal, R. Neogy, and A. Satyanarayan, “Lyra
2: Designing interactive visualizations by demonstration,” IEEE
Transactions on Visualization and Computer Graphics, vol. 27, no. 2,
pp. 304–314, 2021.
[109] J. R. Thompson, Z. Liu, and J. Stasko, “Data animator: Authoring
expressive animated data graphics,” in Proceedings of the 2021 CHI
Conference on Human Factors in Computing Systems, ser. CHI ’21.
ACM, 2021.
[110] M. Conlen, M. Vo, A. Tan, and J. Heer, “Idyll studio: A struc-
tured editor for authoring interactive & data-driven articles,” in
The 34th Annual ACM Symposium on User Interface Software and
Technology, ser. UIST ’21. ACM, 2021, pp. 1–12.
[111] Z. Chen and H. Xia, “CrossData: Leveraging text-data connec-
tions for authoring data documents,” in Proceedings of the 2022
CHI Conference on Human Factors in Computing Systems, ser. CHI
’22. ACM, 2022.
[112] Y. Cao, J. L. E, Z. Chen, and H. Xia, “DataParticles: Block-based
and language-oriented authoring of animated unit visualiza-
tions,” in Proceedings of the 2023 CHI Conference on Human Factors
in Computing Systems, ser. CHI ’23. ACM, 2023.
[113] A. Satyanarayan and J. Heer, “Authoring narrative visualizations
with ellipsis,” Computer Graphics Forum, vol. 33, no. 3, pp. 361–
370, 2014.
[114] A. Satyanarayan, B. Lee, D. Ren, J. Heer, J. Stasko, J. Thompson,
M. Brehmer, and Z. Liu, “Critical reflections on visualization au-
thoring systems,” IEEE Transactions on Visualization and Computer
Graphics, vol. 26, no. 1, pp. 461–471, 2019.
[115] M. Conlen and J. Heer, “Idyll: A markup language for authoring
and publishing interactive articles on the web,” in Proceedings
of the 31st Annual ACM Symposium on User Interface Software and
Technology, ser. UIST ’18. ACM, 2018, pp. 977–989.
[116] A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer,
“Vega-Lite: A grammar of interactive graphics,” IEEE Transactions
on Visualization and Computer Graphics, vol. 23, no. 1, pp. 341–350,
2017.
[117] Y. Fu and J. Stasko, “Supporting data-driven basketball journal-
ism through interactive visualization,” in Proceedings of the 2022
CHI Conference on Human Factors in Computing Systems, ser. CHI
’22. ACM, 2022.
[118] S. Gratzl, A. Lex, N. Gehlenborg, N. Cosgrove, and M. Streit,
“From visual exploration to storytelling and back again,” Com-
puter Graphics Forum, vol. 35, no. 3, pp. 491–500, 2016.
[119] S. Chen, J. Li, G. Andrienko, N. Andrienko, Y. Wang, P. H.
Nguyen, and C. Turkay, “Supporting story synthesis: Bridging
the gap between visual analytics and storytelling,” IEEE Trans-
actions on Visualization and Computer Graphics, vol. 26, no. 7, pp.
2499–2516, 2020.
[120] E. L. Hutchins, J. D. Hollan, and D. A. Norman, “Direct manip-
ulation interfaces,” Human-Computer Interaction, vol. 1, no. 4, pp.
311–338, 1985.
[121] J. Hullman, N. Diakopoulos, E. Momeni, and E. Adar, “Content,
context, and critique: Commenting on a data visualization blog,”
in Proceedings of the 18th ACM Conference on Computer Supported
Cooperative Work amp; Social Computing, ser. CSCW ’15. ACM,
2015, pp. 1170–1175.
[122] N. J. Stroud, E. Van Duyn, and C. Peacock, “News commenters
and news comment readers,” Engaging News Project, pp. 1–21,
2016.
[123] P. J. Boczkowski and E. Mitchelstein, “How users take advantage
of different forms of interactivity on online news sites: Clicking,
e-mailing, and commenting,” Human Communication Research,
vol. 38, no. 1, pp. 1–22, 2012.
[124] E. Dong, H. Du, and L. Gardner, “An interactive web-based
dashboard to track covid-19 in real time.” The Lancet Infectious
Diseases, 2020, vol. 20, no. 5, pp. 533–534.
[125] A. Sarikaya, M. Correll, L. Bartram, M. Tory, and D. Fisher,
“What do we talk about when we talk about dashboards?” IEEE
Transactions on Visualization and Computer Graphics, vol. 25, no. 1,
pp. 682–692, 2019.
[126] M. L. Young, A. Hermida, and J. Fulda, “What makes for great
data journalism?” Journalism Practice, vol. 12, no. 1, pp. 115–135,
2018.
[127] B. Bach, E. Freeman, A. Abdul-Rahman, C. Turkay, S. Khan,
Y. Fan, and M. Chen, “Dashboard design patterns,” IEEE Trans-
actions on Visualization and Computer Graphics, vol. 29, no. 1, pp.
342–352, 2023.
[128] S. Carpendale, “Evaluating information visualizations,” in In-
formation Visualization - Human-Centered Issues and Perspectives,
A. Kerren, J. T. Stasko, J.-D. Fekete, and C. North, Eds. Springer
Berlin Heidelberg, 2008, pp. 19–45.
[129] B. Shneiderman and C. Plaisant, “Strategies for evaluating in-
formation visualization tools: Multi-dimensional in-depth long-
term case studies,” in Proceedings of the 2006 AVI Workshop on
BEyond Time and Errors: Novel Evaluation Methods for Information
Visualization, ser. BELIV ’06. ACM, 2006, pp. 1–7.
[130] M. Sedlmair, M. Meyer, and T. Munzner, “Design study method-
ology: Reflections from the trenches and the stacks,” IEEE Trans-
actions on Visualization and Computer Graphics, vol. 18, no. 12, pp.
2431–2440, 2012.
[131] M. Meyer and J. Dykes, “Criteria for rigor in visualization design
study,” IEEE Transactions on Visualization and Computer Graphics,
vol. 26, no. 1, pp. 87–97, 2020.
[132] J. Zimmerman and J. Forlizzi, Research Through Design in HCI,
2014, pp. 167–189.
[133] J. Zimmerman, J. Forlizzi, and S. Evenson, “Research through
design as a method for interaction design research in hci,” in Pro-
ceedings of the SIGCHI Conference on Human Factors in Computing
Systems, ser. CHI ’07. ACM, 2007, pp. 493–502.
[134] D. Domingo, “Interactivity in the daily routines of online
newsrooms: dealing with an uncomfortable myth,” Journal of
Computer-Mediated Communication, vol. 13, no. 3, pp. 680–704,
2008.
[135] T. Munzner, “Process and pitfalls in writing information visu-
alization research papers,” in Information Visualization: Human-
Centered Issues and Perspectives, A. Kerren, J. T. Stasko, J.-D. Fekete,
and C. North, Eds. Springer Berlin Heidelberg, 2008, pp. 134–
153.
[136] F. Amini, N. H. Riche, B. Lee, A. Monroy-Hernandez, and P. Irani,
“Authoring data-driven videos with dataclips,” IEEE Transactions
on Visualization and Computer Graphics, vol. 23, no. 1, pp. 501–510,
2017.
[137] Z. Chen, S. Ye, X. Chu, H. Xia, H. Zhang, H. Qu, and Y. Wu,
“Augmenting sports videos with viscommentator,” IEEE Trans-
actions on Visualization and Computer Graphics, vol. 28, no. 1, pp.
824–834, 2022.
[138] M. Shin, J. Kim, Y. Han, L. Xie, M. Whitelaw, B. C. Kwon, S. Ko,
and N. Elmqvist, “Roslingifier: Semi-automated storytelling for
animated scatterplots,” IEEE Transactions on Visualization and
Computer Graphics, vol. 29, no. 6, pp. 2980–2995, 2023.
[139] Z. Chen, Q. Yang, X. Xie, J. Beyer, H. Xia, Y. Wu, and H. Pfister,
“Sporthesia: Augmenting sports videos using natural language,”
IEEE Transactions on Visualization and Computer Graphics, vol. 29,
no. 1, pp. 918–928, 2023.
[140] T. Tang, J. Tang, J. Hong, L. Yu, P. Ren, and Y. Wu, “Design
guidelines for augmenting short-form videos using animated
data visualizations,” Journal of Visualization, vol. 23, no. 4, pp.
707–720, 2020.
[141] T. Lin, Z. Chen, Y. Yang, D. Chiappalupi, J. Beyer, and H. Pfister,
“The quest for : Embedded visualization for augmenting basket-
ball game viewing experiences,” IEEE Transactions on Visualization
and Computer Graphics, vol. 29, no. 1, pp. 962–971, 2023.
[142] M. Wattenberg, “Baby names, visualization, and social data anal-
ysis,” in IEEE Symposium on Information Visualization, ser. InfoVis
’05, 2005, pp. 1–7.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
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JOURNAL OF L
A
T
E
X CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 19
[143] B. Lee, E. K. Choe, P. Isenberg, K. Marriott, and J. Stasko, “Reach-
ing broader audiences with data visualization,” IEEE Computer
Graphics and Applications, vol. 40, no. 2, pp. 82–90, 2020.
[144] B. Lee, A. Srinivasan, J. Stasko, M. Tory, and V. Setlur, “Mul-
timodal interaction for data visualization,” in Proceedings of the
2018 International Conference on Advanced Visual Interfaces, ser. AVI
’18. ACM, 2018.
[145] B. Lee, A. Srinivasan, P. Isenberg, and J. Stasko, “Post-WIMP
interaction for information visualization,” Foundations and Trends
in Human-Computer Interaction, vol. 14, no. 1, pp. 1–95, 2021.
[146] S. Reeves, S. Benford, C. O’Malley, and M. Fraser, “Designing the
spectator experience,” in Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, ser. CHI ’05. ACM, 2005,
pp. 741–750.
[147] Q. Zhi, S. Lin, P. Talkad Sukumar, and R. Metoyer, “Gameviews:
Understanding and supporting data-driven sports storytelling,”
in Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems, ser. CHI ’19. ACM, 2019, pp. 1–13.
[148] C. G
¨
org, Z. Liu, J. Kihm, J. Choo, H. Park, and J. Stasko, “Com-
bining computational analyses and interactive visualization for
document exploration and sensemaking in Jigsaw,” IEEE Trans-
actions on Visualization and Computer Graphics, vol. 19, no. 10, pp.
1646–1663, 2013.
[149] C. G
¨
org, Z. Liu, and J. Stasko, “Reflections on the evolution of the
Jigsaw visual analytics system,” Information Visualization, vol. 13,
no. 4, pp. 336–345, 2014.
[150] Y.-a. Kang, C. G
¨
org, and J. Stasko, “How can visual analytics
assist investigative analysis? Design implications from an evalu-
ation,” IEEE Transactions on Visualization and Computer Graphics,
vol. 17, no. 5, pp. 570–583, 2011.
[151] T. Erickson and W. A. Kellogg, “Social translucence,” ACM
Transactions on Computer-Human Interaction, vol. 7, no. 1, pp. 59–
83, 2000.
[152] M. Eslami, A. Rickman, K. Vaccaro, A. Aleyasen, A. Vuong,
K. Karahalios, K. Hamilton, and C. Sandvig, “”I always assumed
that I wasn’t really that close to [her]”: Reasoning about invisible
algorithms in news feeds,” in Proceedings of the 33rd Annual ACM
Conference on Human Factors in Computing Systems, ser. CHI ’15.
ACM, 2015, pp. 153–162.
[153] J. Choo and S. Liu, “Visual analytics for explainable deep learn-
ing,” IEEE Computer Graphics and Applications, vol. 38, no. 4, pp.
84–92, 2018.
[154] W. Samek, T. Wiegand, and K.-R. M
¨
uller, “Explainable artificial
intelligence: Understanding, visualizing and interpreting deep
learning models,” arXiv preprint arXiv:1708.08296, 2017.
[155] F. Hohman, M. Kahng, R. Pienta, and D. H. Chau, “Visual
analytics in deep learning: An interrogative survey for the next
frontiers,” IEEE Transactions on Visualization and Computer Graph-
ics, vol. 25, no. 8, pp. 2674–2693, 2019.
[156] Z. J. Wang, R. Turko, O. Shaikh, H. Park, N. Das, F. Hohman,
M. Kahng, and D. H. Polo Chau, “Cnn explainer: Learning
convolutional neural networks with interactive visualization,”
IEEE Transactions on Visualization and Computer Graphics, vol. 27,
no. 2, pp. 1396–1406, 2021.
[157] J. Burrell, “How the machine ‘thinks’: Understanding opacity in
machine learning algorithms,” Big Data & Society, vol. 3, no. 1,
2016.
[158] M. T. Ribeiro, S. Singh, and C. Guestrin, “”Why should I trust
you?”: Explaining the predictions of any classifier,” in Proceedings
of the 22nd ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, ser. KDD ’16. ACM, 2016, pp. 1135–
1144.
[159] D. Lupton, “Feeling your data: Touch and making sense of
personal digital data,” New Media & Society, vol. 19, no. 10, pp.
1599–1614, 2017.
[160] G. Lupi, S. Posavec, and M. Popova, Dear Data. Princeton
Architectural Press, 2016.
[161] J. Kastrenakes and J. Peters, “Webby awards
2020: the complete winners list,” https:
//www.theverge.com/2020/5/20/21263445/
2020-webby-awards-winners-lil-nas-x-nasa-jon-krasinski,
The Verge, May 2020.
[162] “Story portrait,” https://help.nytimes.com/hc/en-us/articles/
6921803704468, The New York Times, 2020.
[163] S. Munson, S. Lee, and P. Resnick, “Encouraging reading of
diverse political viewpoints with a browser widget,” Proceedings
of the International AAAI Conference on Web and Social Media, vol. 7,
no. 1, pp. 419–428, 2021.
[164] G. R. Hayes, “The relationship of action research to human-
computer interaction,” ACM Transactions on Computer-Human
Interaction, vol. 18, no. 3, pp. 1–20, 2011.
[165] G. Hayes, Knowing by Doing: Action Research as an Approach to
HCI. Springer New York, 2014, pp. 49–68.
[166] VisualizeNews, “India goes to polls,” https://india.visualize.
news/, Indian General Elections 2019, 2019.
[167] N. Nigro, “Hamilton 2.0 dashboard,” https://
securingdemocracy.gmfus.org/hamilton-dashboard/, Alliance
For Securing Democracy, 2022.
[168] L. Nicoletti and S. Sarva, “When women make headlines: A
visual essay about the (mis)representation of women in the
news,” https://pudding.cool/2022/02/women-in-headlines/,
The Pudding, 2021.
[169] N. McCurdy, J. Dykes, and M. Meyer, “Action design research
and visualization design,” in Proceedings of the Sixth Workshop on
Beyond Time and Errors on Novel Evaluation Methods for Visualiza-
tion, ser. BELIV ’16. ACM, 2016, pp. 10–18.
[170] J. Zhou, Y. Zhang, Q. Luo, A. G. Parker, and M. De Choudhury,
“Synthetic lies: Understanding ai-generated misinformation and
evaluating algorithmic and human solutions,” in Proceedings of
the 2023 CHI Conference on Human Factors in Computing Systems,
ser. CHI ’23. ACM, 2023.
[171] K. Shu, D. Mahudeswaran, and H. Liu, “Fakenewstracker: a tool
for fake news collection, detection, and visualization,” Compu-
tational and Mathematical Organization Theory, vol. 25, no. 1, pp.
60–71, 2019.
[172] A. Karduni, I. Cho, R. Wesslen, S. Santhanam, S. Volkova, D. L.
Arendt, S. Shaikh, and W. Dou, “Vulnerable to misinformation?
verifi!” in Proceedings of the 24th International Conference on Intelli-
gent User Interfaces, ser. IUI ’19. ACM, 2019, pp. 312–323.
[173] A. Karduni, R. Wesslen, S. Santhanam, I. Cho, S. Volkova,
D. Arendt, S. Shaikh, and W. Dou, “Can you verifi this? studying
uncertainty and decision-making about misinformation using
visual analytics,” Proceedings of the International AAAI Conference
on Web and Social Media, vol. 12, no. 1, Jun. 2018.
[174] S. Lee, S. Afroz, H. Park, Z. J. Wang, O. Shaikh, V. Sehgal,
A. Peshin, and D. H. Chau, “Misvis: Explaining web misinfor-
mation connections via visual summary,” in Extended Abstracts of
the 2022 CHI Conference on Human Factors in Computing Systems,
ser. CHI EA ’22. ACM, 2022.
[175] D. Lazer, M. Baum, N. Grinberg, L. Friedland, K. Joseph,
W. Hobbs, and C. Mattsson, “Combating fake news: An agenda
for research and action,” 2017.
[176] H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi, “Truth of
varying shades: Analyzing language in fake news and political
fact-checking,” in Proceedings of the 2017 Conference on Empirical
Methods in Natural Language Processing. Association for Compu-
tational Linguistics, Sep. 2017, pp. 2931–2937.
[177] E. Wall, A. Narechania, A. Coscia, J. Paden, and A. Endert, “Left,
right, and gender: Exploring interaction traces to mitigate human
biases,” IEEE Transactions on Visualization and Computer Graphics,
vol. 28, no. 1, pp. 966–975, 2022.
[178] A. Narechania, A. Coscia, E. Wall, and A. Endert, “Lumos:
Increasing awareness of analytic behavior during visual data
analysis,” IEEE Transactions on Visualization and Computer Graph-
ics, vol. 28, no. 1, pp. 1009–1018, 2022.
[179] A. D. Holan, “The principles of the Truth-O-Meter:
PolitiFact’s methodology for independent fact-checking,”
https://www.politifact.com/article/2018/feb/12/
principles-truth-o-meter-politifacts-methodology-i/, Politifact,
2020.
[180] C. R. Sunstein, S. C. Lazzaro, and T. Sharot, “How people update
beliefs about climate change: Good news and bad news,” SSRN
Electronic Journal, 2016.
[181] A. V. Pandey, K. Rall, M. L. Satterthwaite, O. Nov, and E. Bertini,
“How deceptive are deceptive visualizations? an empirical anal-
ysis of common distortion techniques,” in Proceedings of the 33rd
Annual ACM Conference on Human Factors in Computing Systems,
ser. CHI ’15. ACM, 2015, pp. 1469–1478.
[182] A. Fan, Y. Ma, M. Mancenido, and R. Maciejewski, “Annotating
line charts for addressing deception,” in Proceedings of the 2022
CHI Conference on Human Factors in Computing Systems, ser. CHI
’22. ACM, 2022, pp. 1–12.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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JOURNAL OF L
A
T
E
X CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 20
[183] A. McNutt, G. Kindlmann, and M. Correll, “Surfacing visualiza-
tion mirages,” in Proceedings of the 2020 CHI Conference on Human
Factors in Computing Systems, ser. CHI ’20. ACM, 2020, pp. 1–16.
[184] A. K. Hopkins, M. Correll, and A. Satyanarayan, “VisuaLint:
Sketchy in situ annotations of chart construction errors,” Com-
puter Graphics Forum, vol. 39, no. 3, pp. 219–228, 2020.
[185] The White House, “We just learned that President Biden’s
first year in office was the strongest year for economic
growth since 1984,” https://twitter.com/WhiteHouse/status/
1486709480351952901, 2022.
[186] B. E. Rogowitz, L. A. Treinish, and S. Bryson, “How not to lie
with visualization,” Computers in Physics, vol. 10, no. 3, p. 268,
1996.
[187] E. R. Tufte, The Visual Display of Quantitative Information, 2nd ed.
Graphics Press, 2001.
[188] M. A. Borkin, Z. Bylinskii, N. W. Kim, C. M. Bainbridge, C. S.
Yeh, D. Borkin, H. Pfister, and A. Oliva, “Beyond memorability:
Visualization recognition and recall,” IEEE Transactions on Visual-
ization and Computer Graphics, vol. 22, no. 1, pp. 519–528, 2016.
[189] H.-K. Kong, Z. Liu, and K. Karahalios, “Frames and slants in
titles of visualizations on controversial topics,” in Proceedings of
the 2018 CHI Conference on Human Factors in Computing Systems,
ser. CHI ’18. ACM, 2018, pp. 1–12.
[190] ——, “Trust and recall of information across varying degrees of
title-visualization misalignment,” in Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems, ser. CHI ’19.
ACM, 2019, pp. 1–13.
[191] P.-M. Law, A. Endert, and J. Stasko, “Characterizing automated
data insights,” in 2020 IEEE Visualization Conference (VIS), 2020,
pp. 171–175.
[192] C. Oh, J. Choi, S. Lee, S. Park, D. Kim, J. Song, D. Kim, J. Lee,
and B. Suh, “Understanding user perception of automated news
generation system,” in Proceedings of the 2020 CHI Conference on
Human Factors in Computing Systems, ser. CHI ’20. ACM, 2020,
pp. 1–13.
[193] Y. Wang, Z. Sun, H. Zhang, W. Cui, K. Xu, X. Ma, and D. Zhang,
“DataShot: Automatic generation of fact sheets from tabular
data,” IEEE Transactions on Visualization and Computer Graphics,
vol. 26, no. 1, pp. 895–905, 2020.
[194] C. Bryan, K.-L. Ma, and J. Woodring, “Temporal summary
images: An approach to narrative visualization via interactive
annotation generation and placement,” IEEE Transactions on Visu-
alization and Computer Graphics, vol. 23, no. 1, pp. 511–520, 2017.
[195] D. Shi, X. Xu, F. Sun, Y. Shi, and N. Cao, “Calliope: Automatic vi-
sual data story generation from a spreadsheet,” IEEE Transactions
on Visualization and Computer Graphics, vol. 27, no. 2, pp. 453–463,
2021.
[196] A. Srinivasan, S. M. Drucker, A. Endert, and J. Stasko, “Augment-
ing visualizations with interactive data facts to facilitate interpre-
tation and communication,” IEEE Transactions on Visualization and
Computer Graphics, vol. 25, no. 1, pp. 672–681, 2019.
[197] I. Bogost, S. Ferrari, and B. Schweizer, Newsgames: Journalism at
Play, ser. The MIT Press. MIT Press, 2012.
[198] N. Diakopoulos, F. Kivran-Swaine, and M. Naaman, Playable data:
characterizing the design space of game-y infographics, 2011.
[199] P. Isenberg, B. Lee, H. Qu, and M. Cordeil, “Immersive visual
data stories,” in Immersive Analytics, K. Marriott, F. Schreiber,
T. Dwyer, K. Klein, N. H. Riche, T. Itoh, W. Stuerzlinger, and
B. H. Thomas, Eds. Cham: Springer International Publishing,
2018, pp. 165–184.
[200] M. Broussard, “Archiving data journalism.” O’Reilly Media,
2012.
[201] K. Boss and M. Broussard, “Challenges of archiving and preserv-
ing born-digital news applications,” IFLA Journal, vol. 43, no. 2,
pp. 150–157, 2017.
[202] R. Kosara, “The bits are rotting in the state of
data journalism,” https://eagereyes.org/blog/2016/
the-bits-are-rotting-in-the-state-of-data-journalism, eagereyes,
Jul 2016.
[203] M. Broussard, “Preserving news apps present huge challenges,”
Newspaper Research Journal, vol. 36, no. 3, pp. 299–313, 2015.
[204] M. Broussard and K. Boss, “Saving data journalism,” Digital
Journalism, vol. 6, no. 9, pp. 1206–1221, 2018.
[205] L. Ahva and S. Steensen, “Journalism theory,” in The Handbook of
Journalism Studies, 2nd ed., K. Wahl-Jorgensen and T. Hanitzsch,
Eds. Routledge, 2019, pp. 38–54.
[206] E. M. Peck, S. E. Ayuso, and O. El-Etr, “Data is personal: Attitudes
and perceptions of data visualization in rural pennsylvania,”
in Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems, ser. CHI ’19. ACM, 2019, pp. 1–12.
[207] B. Alper, N. H. Riche, F. Chevalier, J. Boy, and M. Sezgin, “Visu-
alization literacy at elementary school,” in Proceedings of the 2017
CHI Conference on Human Factors in Computing Systems, ser. CHI
’17. ACM, 2017, pp. 5485–5497.
[208] M. Correll, “Position paper: Are we making progress in visualiza-
tion research?” in 2022 IEEE Evaluation and Beyond - Methodological
Approaches for Visualization (BELIV), 2022, pp. 1–10.
Yu Fu is a Ph.D. student at Georgia Tech’s
School of Interactive Computing and a member
of the Information Interfaces Group. His research
lies at the intersection of information visualiza-
tion and journalism, with a focus on leveraging
visualization to help journalists and their audi-
ences cope with emerging challenges. He re-
ceived his B.S. and M.S. in Electrical Engineer-
ing. He also worked as a journalist.
John Stasko is a Regents Professor in the
School of Interactive Computing and the Director
of the Information Interfaces Research Group
at the Georgia Institute of Technology. His re-
search is in the areas of information visualiza-
tion and visual analytics, approaching each from
a human-computer interaction perspective. He
was named an IEEE Fellow in 2014 and an ACM
Fellow in 2022.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3287585
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 07:43:48 UTC from IEEE Xplore. Restrictions apply.